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Whole Exome Sequencing

Final evidence report

October 21, 2019

Health Technology Assessment Program (HTA)

Washington State Health Care Authority

PO Box 42712 Olympia, WA 98504-2712

(360) 725-5126 www.hca.wa.gov/hta

[email protected]

http://www.hca.wa.gov/hta

Prepared by:

RTI International–University of North Carolina Evidence-based Practice Center

Research Triangle Park, NC 27709 www.rti.org

This evidence report is based on research conducted by the RTI-University of North Carolina

Evidence-based Practice Center through a contract between RTI International and the State of

Washington Health Care Authority (HCA). The findings and conclusions in this document are

those of the authors, who are responsible for its contents. The findings and conclusions do not

represent the views of the Washington HCA and no statement in this report should be construed

as an official position of Washington HCA.

The information in this report is intended to help the State of Washington’s independent Health

Technology Clinical Committee make well-informed coverage determinations. This report is not

intended to be a substitute for the application of clinical judgment. Anyone who makes decisions

concerning the provision of clinical care should consider this report in the same way as any

medical reference and in conjunction with all other pertinent information (i.e., in the context of

available resources and circumstances presented by individual patients).

This document is in the public domain and may be used and reprinted without permission except

those copyrighted materials that are clearly noted in the document. Further reproduction of those

copyrighted materials is prohibited without the specific permission of copyright holders.

None of the individuals involved in producing this report reported any financial or non-financial

conflicts of interest regarding the topic presented in this report.

Acknowledgments

The following individuals contributed to this report:

Lead Investigator: Nedra Whitehead, PhD

Co-Investigators: Leila Kahwati, MD, MPH, Amy Moore, PhD

Project Coordinator/Analyst: Sara Kennedy, MPH

Analyst: Christine Hill, MPA

Scientific Reviewer: Rachel Palmieri Weber, PhD

Librarian: Christiane Voisin, MSLS

Editing and Document Preparation: Loraine Monroe; Staci Rachman, BA; Laura Small, BA

External Peer Reviewers: Madhuri Hedge, PhD, FACMG, Vice President and Chief

Scientific Officer, PerkinElmer, Inc., Adjunct Professor Emory

University;

Karen Weck, MD, FCAP; Professor, University of North Carolina

at Chapel Hill.

http://www.rti.org/

WA – Health Technology Assessment October 21, 2019

Final

Whole exome sequencing: final evidence report Page i

Contents

Contents ............................................................................................................................ i List of Appendices ............................................................................................................. iii List of Figures .................................................................................................................... iii

List of Tables ..................................................................................................................... iii List of Abbreviations .......................................................................................................... v

Executive Summary ................................................................................................................ ES-1 Structured Abstract ..............................................................................................................ES-1 ES 1. Background ..............................................................................................................ES-2

ES 1.1 Condition Description ....................................................................................... ES-2 ES 1.2 Disease Burden ................................................................................................. ES-2 ES 1.3 Technology Description .................................................................................... ES-3 ES 1.4 Regulatory Status .............................................................................................. ES-3 ES 1.5 Policy Context ................................................................................................... ES-3

ES 2. Methods....................................................................................................................ES-4 ES 2.1 Research Questions and Analytic Framework .................................................. ES-4

ES 2.2 Data Sources and Search ................................................................................... ES-5 ES 2.3 Study Selection ................................................................................................. ES-6

ES 2.4 Data Abstraction and Risk of Bias Assessment ................................................ ES-6 ES 2.5 Data Synthesis and Quality of Evidence Assessment ....................................... ES-7

ES 3. Results ......................................................................................................................ES-7

ES 3.1 Literature Yield ................................................................................................. ES-7 ES 3.2 Contextual Questions on Diagnostic Yield ....................................................... ES-7

ES 3.3 Key Question 1: Effectiveness (Clinical Utility) .............................................. ES-8

ES 3.4 Key Question 2: Effectiveness (Health Outcomes) .......................................... ES-9

ES 3.5 Key Question 3: Safety and Harms ................................................................... ES-9 ES 3.6 Key Question 4: Costs ..................................................................................... ES-10

ES 4. Discussion ..............................................................................................................ES-11 ES 4.1 Summary of the Evidence ............................................................................... ES-11 ES 4.2 Limitations of the Evidence Base ................................................................... ES-12

ES 4.3 Clinical Practice Guidelines and Related Health Technology Assessments .. ES-12 ES 4.4 Selected Payer Coverage Policies ................................................................... ES-13 ES 4.5 Limitations of this HTA .................................................................................. ES-13

ES 4.6 Ongoing Research ........................................................................................... ES-13 ES 5. Conclusion .............................................................................................................ES-14

Full Technical Report ................................................................................................................... 1 1. Background ....................................................................................................................1

1.1 Condition Description ................................................................................................. 1 1.2 Disease Burden ............................................................................................................ 2 1.3 Technology Description .............................................................................................. 2

1.4 Regulatory Status ........................................................................................................ 5 1.5 Policy Context ............................................................................................................. 5 1.6 Washington State Agency Utilization Data ................................................................. 5


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Whole exome sequencing: final evidence report Page ii

2. Methods..........................................................................................................................6

2.1 Research Questions and Analytic Framework ............................................................ 6 2.2 Data Sources and Searches .......................................................................................... 8

2.3 Study Selection ............................................................................................................ 8 2.4 Data Abstraction and Risk of Bias Assessment ........................................................ 12 2.5 Data Synthesis and Quality of Evidence Rating ....................................................... 12

3. Results ..........................................................................................................................13 3.1 Literature Search ...................................................................................................... 13

3.2 Contextual Questions ............................................................................................... 15 3.3 Key Question 1: Effectiveness (Clinical Utility) ...................................................... 17 3.4 Key Question 2: Effectiveness (Health Outcomes) ................................................... 29 3.5 Key Question 3: Safety and Harms ......................................................................... 33 3.6 Key Question 4: Costs .............................................................................................. 38

4. Discussion ....................................................................................................................53 4.1 Summary of the Evidence ......................................................................................... 53

4.2 Limitations of the Evidence Base .............................................................................. 55 4.3 Clinical Practice Guidelines and Related Health Technology Assessments ............. 56

4.4 Selected Payer Coverage Policies ............................................................................. 57 4.5 Limitations of This HTA ........................................................................................... 64

4.6 Ongoing Research ..................................................................................................... 64 5. Conclusion ...................................................................................................................66 6. References ....................................................................................................................66


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Whole exome sequencing: final evidence report Page iii

List of Appendices

Appendix A. State of Washington Health Care Authority Utilization Data ....................... A-1

Appendix B. Search Strategy ................................................................................................... B-1

Appendix C. Evidence Tables .................................................................................................. C-1

Appendix D. Excluded Articles ................................................................................................ D-1

Appendix E. Individual Study Risk of Bias Assessments ...................................................... E-1

Appendix F. Studies Reporting Diagnostic Yield ................................................................... F-1

Appendix G. Detailed GRADE Assessments .......................................................................... G-1

List of Figures

Figure ES-1. Analytic framework for HTA on whole exome sequencing ............................. ES-5

Figure ES-2. Summary of evidence from whole exome sequencing HTA .......................... ES-11

Figure 1. Variant-filtering process ......................................................................................... 4

Figure 2. Analytic framework for HTA on whole exome sequencing ................................... 7

Figure 3. Study flow diagram for HTA on whole exome sequencing ................................. 14

Figure 4. Summary of evidence from whole exome sequencing HTA ................................ 54

List of Tables

Table ES-1. Indications for diagnostic testing from 2012 policy statement entitled “Points to

Consider in the Clinical Application of Genomic Sequencing” ..................... ES-13

Table ES-2. Overview of payer coverage policies for whole exome sequencing ............... ES-13

Table 1. Population, intervention, comparator, outcomes, period, setting, and other study-

selection criteria for HTA on WES ......................................................................... 8

Table 2. Certainty of evidence grades and definitions98 ..................................................... 13

Table 3. Systematic reviews of the diagnostic yield of WES ............................................. 15

Table 4. Diagnostic yield for specific disorders ................................................................. 16

Table 5. Summary of characteristics and outcomes for studies evaluating clinical utility

among studies enrolling diverse populations ........................................................ 20


among studies enrolling patients with epilepsy .................................................... 25


among studies enrolling other single phenotype populations ............................... 28

Table 8. Summary of characteristics and findings for studies evaluating health outcomes 30


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Whole exome sequencing: final evidence report Page iv

Table 9. Study and population characteristics of the one controlled cohort studies

evaluating missed diagnoses in WES compared to standard genetic testing ........ 34

Table 10. Study and population characteristics of the 22 studies that reported on the

frequency of ACMG-defined medically actionable variants and participants’

choice to receive them .......................................................................................... 35

Table 11. Study and population characteristics of the 8 studies reporting on psychosocial

harms and reactions among patients and parents of patients who underwent WES

............................................................................................................................... 37

Table 12. Summary of characteristics for studies evaluating cost outcomes related to whole

exome sequencing ................................................................................................. 40

Table 13. Summary of cost-related outcomes from studies enrolling diverse phenotype

populations ............................................................................................................ 46

Table 14. Summary of cost-related outcomes from studies enrolling single-phenotype

populations ............................................................................................................ 51

Table 15. Indications and contraindications for clinical exome sequencing from the

American Academy of Neurology’s Model Coverage Policies ............................ 56

Table 16. Indications for diagnostic testing from 2012 policy statement entitled “Points to

Consider in the Clinical Application of Genomic Sequencing” ........................... 57

Table 17. Payer coverage policies for whole exome sequencing ......................................... 58

Table 18. Summary of ongoing whole exome sequencing studies ....................................... 64


Final

Whole exome sequencing: final evidence report Page v

List of Abbreviations

ACMG American College of Medical Genetics and Genomics

CI Confidence interval

CMA Chromosomal microarray analysis

CNV Copy number variants

CPG Clinical practice guidelines

ES Executive summary

HTA Health technology assessment

NR Not reported

NS Not significant

QALY Quality-adjusted life year

U.K. United Kingdom

U.S. United States

WES Whole exome sequencing

WGS Whole genome sequencing


Final

Whole exome sequencing: final evidence report Page ES-1

Executive Summary

Structured Abstract

Purpose: To conduct a health technology assessment (HTA) on the efficacy, safety, and cost of

whole exome sequencing (WES).

Data Sources: PubMed and Embase from inception through March 14, 2019; clinical trial

registry; government, payor, and clinical specialty organization websites; hand searches of

systematic reviews.

Study Selection: Using a priori criteria, we selected English-language primary research studies

that were conducted in very highly developed countries that reported clinical utility (i.e., changes

in medical management resulting from diagnosis), health outcomes, safety outcomes (such as

secondary findings), or cost outcomes. We selected trials, cohort studies (controlled or

uncontrolled), or case series with 5 or more participants. To address a separate contextual

question on diagnostic yield, we also identified studies from our search that reported this

outcome.

Data Extraction: One research team member extracted data and a second checked for accuracy.

Two investigators independently assessed risk of bias of included studies. We rated the certainty

of the evidence using the Grading of Recommendations, Assessment, Development, and

Evaluation (GRADE) approach.

Data Synthesis: We identified 99 studies for the contextual question on diagnostic yield. On

average, 38% of patients for whom WES is performed receive a diagnosis. Annual reanalysis of

WES data increases the diagnostic yield. Diagnostic yield was highest among patients with

phenotypes exclusively or predominantly of genetic origin, such as childhood-onset muscle

disorders.

We included 57 studies that reported 1 or more clinical utility, health, safety, or cost outcome. A

diagnosis from WES resulted in a change in clinical management of 12% to 100% across 18

studies that enrolled diverse phenotypes and 0% to 31% across 5 studies enrolling participants

with epilepsy. Seven studies reported on diverse health outcomes. Four studies among

hospitalized pediatric patients reported mortality, which ranged from 17% to 57%. Management

changes based on WES resulted in improved seizure control or behavior management in 0% to

3% of patients with epilepsy. The pooled proportion of patients with a medically actionable

secondary finding was 3.9% across 13 studies; most patients and families did not experience

psychosocial harms from receiving negative or uncertain WES results. The cost of a WES test

ranged from $1,000 to $15,000 across 15 studies. In both single-phenotype and diverse-

phenotype populations, testing pathways that included WES identified more diagnoses and either

cost less or cost somewhat more (highest reported estimate was $8,599 more) per additional

diagnosis. Pathways with earlier WES testing were more likely to be cost savings compared to

pathways that used WES later in the testing pathway or used WES as a last-resort strategy.


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Limitations: Most of the evidence is from uncontrolled, retrospective, observational studies.

Conclusions: WES increases the yield of molecular diagnosis over standard diagnostic testing.

A diagnosis from WES changes clinical management for some patients, but our certainty in the

estimate of this frequency is very low. The evidence regarding the impact of WES testing on

health and most safety outcomes is limited, though we have low certainty that the proportion of

patients tested who receive a medically actionable secondary finding is about 3.9%. WES may be

cost-effective in terms of diagnostic success, but our certainty is very low.

ES 1. Background

We designed this health technology assessment (HTA) to assist the State of Washington’s

independent Health Technology Clinical Committee with determining coverage for whole exome

sequencing (WES).

ES 1.1 Condition Description

Whole exome sequencing (WES) may be indicated for testing for a wide range of genetic

diseases. This test is most commonly used when a patient is suspected of having a genetic

disorder, but the signs or symptoms are not recognizable as a specific genetic condition. This test

is also used when the patient’s phenotype could be consistent with multiple genetic disorders or a

blended phenotype. A variant in the sequence of a gene may or may not affect the gene’s

function or result in symptoms. Variants that cause malformation, dysfunction, or disorders are

termed pathogenic variants. Indications that a symptom or phenotype may be related to a

pathogenic genetic variant include dysmorphic features, multiple anomalies, unexplained

neurocognitive impairment, multifocal presentation, earlier or more severe onset of common

symptoms, or a family history of similarly affected individuals.1,2 Although genetic conditions

are often thought of has having onset during infancy or childhood, many genetic disorders first

become symptomatic in adulthood. Further, some conditions with pediatric onset may not be

diagnosed until adulthood.

Because WES targets the entire exome, it may also identify genetic disorders other than those

that cause the patient's phenotype, some of which require specific medical management. For

example, the identification of a pathogenic mutation in the BRCA2 gene would prompt early,

frequent screening for breast cancer or other preventive measures.1 In 2013, the American

College of Medical Genetics and Genomics (ACMG) recommended specifically looking for

pathogenic and likely pathogenic variants in 56 genes for which medical management guidelines

were available, in order to standardize the reporting of secondary findings.2

ES 1.2 Disease Burden

There are more than 6,000 human genetic diseases.3 Although genetic diseases are individually

rare, they collectively affect approximately 1 in 17 individuals.4 One study estimated that the

range of total inpatient charges for United States (U.S.) pediatric patients related to suspected

genetic diseases in 2012 was US$ 14 to US$ 57 billion—11% to 46% of all pediatric inpatient

charges.5


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ES 1.3 Technology Description

WES identifies the DNA base-pair sequences of the protein-coding regions of the genome.6 WES

is primarily used to identify small changes in base-pair sequences that disrupt protein function

and cause disease; however, improved bioinformatics has increased the ability to identify

chromosomal copy number variants (i.e., larger deletions or duplications involving larger

stretches of DNA) from sequenced data and also changes in the mitochondrial genome.

Diagnostic WES testing is ordered by a physician or other health care professional and is

conducted in a clinical diagnostic laboratory to aid in the diagnosis of a patient. Parents’ or

siblings’ genes may be sequenced to help interpret identified variants (Trio WES).

WES uses next-generation sequencing (NGS) technologies; NGS makes many copies of the

target genome, then cuts them into random sequences, and simultaneously sequences the

resulting fragments. After this sequencing step, WES requires a series of bioinformatics analyses

to interpret the sequencing. These analyses are described in detail in Section 1.3 of the full

report. A clinical laboratory report for WES usually includes all pathogenic or likely pathogenic

variants identified in genes associated with the clinical phenotype of the patient and their

interpretation as primary findings, and any ACMG-defined medically actionable variants as

secondary findings. Variants with unknown pathogenicity that may be relevant to the patient's

phenotype may also be reported.

The use of WES within clinical practice is still evolving in terms of how and where it is used

within a diagnostic testing pathway for individual patients. Typically, WES is used when a

monogenic disorder is suspected but when the patient’s phenotype does not suggest a specific

disorder for testing. WES can replace most single and multigene panel testing, but up until

recently could not replace chromosomal microarray analysis (CMA) for the detection of copy

number variants.

ES 1.4 Regulatory Status

The U.S. Food and Drug Administration (FDA) approves the sequencing platforms which are

used to conduct clinical NGS, including WES. FDA approval is based on the demonstration of

analytic validity, in other words that the sequencing machines correctly sequence DNA

specimens. The FDA does not regulate WES as a diagnostic test, which involves both the

sequencing component and the bioinformatics and variant interpretation component. WES is

conducted by laboratories that are accredited by the Clinical and Laboratory Improvement Act

(CLIA) to conduct high complexity testing. Because of the equipment and software involved

(particularly for the bioinformatics platform), this test is generally only conducted in laboratories

associated with large, tertiary medical centers or commercial genetics laboratories.

ES 1.5 Policy Context

The State of Washington HCA selected this topic for review because of high concerns for safety

and medium concerns for efficacy and cost.


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ES 2. Methods

This section describes the methods we used to conduct this HTA.

ES 2.1 Research Questions and Analytic Framework

We developed the following research questions to guide the systematic evidence review of

primary research studies:

Key Question 1: Effectiveness (Clinical Utility)

1a. In what proportion of patients does testing with WES result in a clinically actionable finding

(i.e., the diagnosis resulting from WES leads to something that can be treated, prevented, or

mitigated)?

1b. In what proportion of patients does testing with WES result in an actual change to the

patient’s medical management (medication or therapies, follow-up testing, medical monitoring)

or genetic counseling (reproductive risks or risks of other family members)?

1c. What is the effect of testing pathways that include WES on medical management or genetic

risk counseling compared to testing pathways that do not include WES?

Key Question 2: Effectiveness (Health Outcomes)

2a.: What are the health outcomes, including mortality, among patients who have WES testing?

2b: What are the health outcomes, including mortality, of patients who receive testing pathways

that include WES compared to alternative testing pathways with or without WES?

Key Question 3: Safety and Harms

3a: How many patients receive erroneous results after WES testing, either false positive or false

negative results? What harms are caused by these test results and how many patients experience

these harms?

3b: What harms are caused by uncertain WES results or a lack of diagnosis after WES testing?

3c: How many patients receive reports on ACMG-defined medically actionable variants after

WES testing? What harms do they experience, and how many patients experience these harms?

3d: How frequently do WES results cause harm to family relationships?

Key Question 4: Cost

4a: What is the cost of WES testing?

4b. What is the cost per diagnosis of pathways that include WES testing?


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4c: What is the cost per additional diagnosis, comparing a pathway with WES to an alternative

pathway with or without WES?

4d: What is the cost-effectiveness of testing with WES?

We also created the analytic framework, shown in ES-Figure 1, to guide our review.

Figure ES-1. Analytic framework for HTA on whole exome sequencing

Abbreviations: CMA=chromosomal microarray; ECG=electrocardiography; EEG=electroencephalography;

EMG=electromyography; KQ = key question; WES=whole exome sequencing

In addition, we addressed the following contextual questions, which were not systematically

reviewed and therefore are not shown in the analytic framework.

Contextual Question 1: What is the diagnostic yield of WES either alone or as part of a testing

pathway and what are the factors (e.g., phenotypes being tested, testing platforms and

bioinformatics analysis used) that contribute to variation in diagnostic yields?

Contextual Question 2: How often does WES return variants of uncertain clinical significance

and what impact does repeat bioinformatics analysis have on diagnostic yield?

ES 2.2 Data Sources and Search

We searched MEDLINE® (via PubMed), Embase, and a clinical trials registry for relevant

English-language studies from inception to March 14, 2019. We searched the Centers for

Medicare and Medicaid Services and FDA websites, selected payer and health care professional

Persons with clinical signs/

symptoms suspected of

having a genetic condition

Single gene testing

Multi-gene panel

WES

Single gene testing 1


WES

WES

WESCMA

Alone or in combination:CMA, single

gene testing, multi-gene

panel

History, exam, non-genetic

testing (laboratory,

imaging, EEG, ECG, EMG)

WES Repeat WES

Health Outcomes

Diagnosis

Clinical Utility/

Changes in Management

KQ 1

KQ 2

KQ 3

HarmsFalse Positives, False Negatives

Incidental FindingsPsychological Impact of No Diagnosis, Uncertain

Results, Unexpected Information

CostsCost per diagnosis

Cost per additional diagnosisCost-effectiveness

KQ 4

No genetic testing

Comparator Path 1

Path A

Path B

Path C

Path D

Path E

Comparator Path 2


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society websites, and websites of other organizations. We used medical subject headings (MeSH

terms) and text words associated with whole exome fusion. The detailed search strategy is in

Appendix B.

ES 2.3 Study Selection

Two reviewers independently screened titles and abstracts and full-text articles based on the

following study inclusion criteria (complete details are in Table 1 of the Full Technical Report).

Population: adults or children with suspected genetic disease

Intervention: WES used in a clinical diagnostic context either alone or as part of a

testing strategy that includes other clinical laboratory, imaging, or other diagnostic

investigations. Whole genome sequencing (WGS) was not included within the scope

of this HTA.

Comparator(s): Standard clinical diagnostic investigation (i.e., usual care), single-

gene or multigene panel testing, chromosomal microarray analysis (CMA), and WES

used in different places within the testing pathway. However, we did not require

studies to have a comparator testing strategy.

Outcomes: clinical utility (results could or were used to change clinical management,

further diagnostic testing, or risk counseling or testing of family members, including

reproductive counseling); health outcomes (mortality, length of survival, morbidity,

cognitive ability, functional outcomes); safety and harms (misdiagnosis, proportion

with ACMG-defined medically actionable variants, psychosocial harms, and

employment or insurance discrimination), and cost outcomes (cost of WES test, cost

per patient of strategy with WES, cost per diagnosis, cost per additional

diagnosis[compared to other strategies], cost effectiveness).

Setting(s): Inpatient or outpatient clinical settings from countries with a development

rating designated as very high on the United Nations Human Development Index.

Study Design: Single-arm or controlled clinical trials or observational cohort studies

with more than 10 participants, case-control studies, case series (between 5 to 10

participants), cost-benefit analyses, cost-utility analyses, cost-effectiveness analyses,

modeling studies, and qualitative research studies (for safety and harms outcomes

only).

Other: English-language, published in 2010 or later (WES was not used clinically

before this time)

ES 2.4 Data Abstraction and Risk of Bias Assessment

Two team members extracted relevant study data into a structured abstraction form, and the lead

investigator checked those data for accuracy. Two team members conducted independent risk of


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bias assessments on all included studies. Risk of bias was assessed as high, some concerns, or

low for each separate outcome domain: clinical utility, health outcomes, and safety outcomes.

We assessed the risk of bias for cost outcomes with the Quality of Health Economic Studies

Instrument.7

ES 2.5 Data Synthesis and Quality of Evidence Assessment

We qualitatively synthesized study characteristics and results for each research question in

tabular and narrative formats. We used Stata (version 15) to conduct quantitative pooling of the

diagnostic yield estimate for contextual question 1. We were not able to conduct quantitative

syntheses for any of the key questions because of the clinical and methodological heterogeneity

in this evidence base. We graded the certainty of evidence for each outcome using the Grading of

Recommendations Assessment, Development, and Evaluation (GRADE) approach.8 With

GRADE, the certainty of evidence can be graded as very low, low, moderate, or high based on

imprecision, inconsistency, and study limitations. We note that the GRADE framework was

initially developed for RCTs of interventions; it may not be well suited for assessing the strength

of evidence for genetic testing.

ES 3. Results

ES 3.1 Literature Yield

We included a total of 57 studies from 60 publications published between 2014 and 2019. Thirty

studies provided evidence on clinical utility (KQ1), 7 studies provided evidence on health

outcomes (KQ2), 26 studies provided evidence on safety outcomes (KQ3), and 17 studies

provided evidence on cost outcomes (KQ4).

ES 3.2 Contextual Questions on Diagnostic Yield

Four systematic reviews9-12 and 99 individual studies (see Appendix F) provided information on

the diagnostic yield of WES. Some studies enrolled patients with diverse phenotypes, while

others enrolled patients with a single phenotype (e.g., epilepsy). The degree of diagnostic testing

prior to WES testing that was received by participants enrolled in these studies varied; most had

received some initial diagnostic evaluation (specialty consultation,13-25 laboratory, imaging).

Many had also received some genetic testing (e.g., single or multigene panels, CMA).

We calculated the pooled estimate for diagnostic yield from the 99 individual studies as 38%

(95% CI, 35.7% to 40.6%). We calculated the pooled diagnostic yield of traditional testing

pathways (4 studies16,20,26,27) as 21% (95% CI, 5.6% to 36.4%) and the diagnostic yield of gene

panels (6 studies26,28-32) as 27% (95% CI, 13.7% to 40.5%). The likelihood of a genetic cause and

therefore the diagnostic yield of WES varied by patient age and phenotype. The diagnostic yield

decreased as the age of participants increased: 42% among infants, 38% among children, and

20% among adults. There were 7 disorders or groups of related disorders for which there were

more than 2 studies of diagnostic yield. The diagnostic yield for these disorders ranged from

29% for participants with an intellectual or developmental disability to 48% for those with limb-

girdle muscular dystrophy.


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Reanalysis of WES data using updated variant call algorithms and newly discovered information

on the pathogenicity of genes or variants increases diagnostic yield. Among the 8 studies that

examined the yield from reanalysis, 17% of previously undiagnosed patients were diagnosed by

reanalysis of existing WES data.13,21,33-38

ES 3.3 Key Question 1: Effectiveness (Clinical Utility)

Thirty studies (in 33 publications) reported on clinical utility outcomes.13-16,18,19,22,24,25,27,28,32,34,39-

58 Most studies were single-arm observational cohort studies and 16 were conducted in the U.S.

Fourteen studies were rated as having a high risk of bias, 15 as having some risk of bias, and risk

of bias was not assessed for one qualitative study.50 Ten studies had all or some industry-

funding, but this characteristic was not uniformly reported by all studies.15,19,22,25,27,39,44,45,50,53

Enrollment in 29 studies was limited by age group: 3 studies28,40,56 only included infants, 13 only

included children, and 150 only included adults. The remaining 12 studies included both adults

and children.18,32,34,39,41-46,48,57 Eighteen of the included studies performed WES on patients with

diverse phenotypes. The remaining studies enrolled single-phenotype participants. Five studies

included patients with epilepsy,27,28,32,34,58 and 7 studies included patients with another

phenotype: familial hypercholesterolemia,50 intellectual developmental disorder,44 malignant

infantile osteopetrosis,53 kidney transplant,48 young onset nephrolithiasis,41 neurodevelopmental

disorders,25 and short stature.51

The key findings are:

Among studies that enrolled patients with diverse phenotypes (18 studies):

o A WES diagnosis changed clinical management for between 12% to 100%

o A WES diagnosis changed medication for between 5% to 25%

o A WES diagnosis resulted in counseling and genetic testing for family members for

between 4% and 97%

Among studies that enrolled patients with epilepsy (5 studies):



Among studies that enrolled patients with a single phenotype (7 studies), all reported

some changes in clinical management following a WES diagnosis, but the data was too

heterogenous to synthesize into a single range.

We assessed the certainty of evidence related to all clinical utility outcomes as very low because

of study designs, study limitations, inconsistency, and imprecision.


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ES 3.4 Key Question 2: Effectiveness (Health Outcomes)

Seven studies reported on health outcomes.22,32,34,40,53,56,58 One study was a case series,53 one

study was a controlled observational cohort,22 and the rest were single-arm observational cohort

studies. Three studies were conducted in the U.S.40,56,58 Two studies had some industry

funding,22,56 and 4 studies had no industry funding.32,34,40,58 Two studies did not specifically list

any study funders.32,58 Three studies only included probands under the age of 18,22,53,58 and 2

studies only included infants.40,56 Two studies included both adults and children.32,34 Three of the

included studies performed WES on probands with diverse phenotypes,22,40,56 and 3 studies

included probands with epilepsy.32,34,58 The remaining study included only probands with a

clinical diagnosis of malignant infantile osteopetrosis.53


Mortality ranged from 17% to 57%, but the studies that reported mortality were

conducted among infants in NICUs or hospitalized children with acute illness.

Among patients with epilepsy, management changes resulting from WES diagnosis

improved seizure control or behavior management in 0% to 3% of study participants.

We were unable to assess the certainty of evidence related to health outcomes because of very

serious limitations in the study designs and reporting of outcomes.

ES 3.5 Key Question 3: Safety and Harms

Twenty-six studies provided evidence on the harms associated with WES.20,24,40,42,43,50,54,58-76

Twenty quantitative studies20,24,40,42,43,54,58,60,61,63-66,69-74,76 were single-arm observational cohort

studies. Twelve had a low risk of bias,42,54,60,63,64,66,69-74 6 had some risk of bias,20,24,40,43,50,61,65

and 2 had a high risk of bias.58,76 The single-modeling study75 was rated as having some risk of

bias. We did not assess the risk of bias for the 5 qualitative research studies.50,59,62,67,68 One study

received some industry funding50,72 and 4 were completely funded by industry50,61,64,71; the rest

either had no industry funding or this information was not reported. Nineteen studies were

conducted in the U.S.24,40,43,50,54,58-60,62-69,73,74,76


Two percent of patients diagnosed using standard testing were not diagnosed by

WES. The patients not diagnosed with WES had genetic variants that were not

diagnosed well by WES technology at the time the study was done.

We calculated the pooled percent of patients with an ACMG-defined medically

actionable variant to be 3.9% (95% CI, 2.4% to 5.3%) across 13 studies that provided

data suitable for use in pooling. Of the remaining studies, 4 reported 0% with

actionable variants,20,42,62,70 and the other 5 reported between 1% and 10%.24,54,61,68,75


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Most patients or parents of patients did not experience psychosocial harms from

receiving negative or uncertain WES results; these findings come primarily from

qualitative research studies.

We assessed the certainty of evidence related to the frequency of ACMG-defined medically

actionable variants as low because of study designs and study limitations. We did not assess the

certainty of other reported safety outcomes as they were too heterogenous or largely reported

from qualitative research studies.

ES 3.6 Key Question 4: Costs

Seventeen studies (reported in 20 publications) reported cost-related outcomes.13-28,77-80 Two of

the 17 studies were conducted in the U.S.24,25 Eight studies were funded in part by industry

funding;13-17,19,22,25,27,78,79 the rest were government agency funded or the source of funding was

not clear. Four studies used simulation or modeling to derive cost-related outcomes.17,23,28,79 One

study used a controlled cohort design26 and the remaining 12 studies used a single-arm

observational cohort design. We assessed 6 studies as having a high risk of bias,17,18,24,25,77,78 and

the rest we assessed as having some risk for bias.

Three studies were conducted among populations that included both children and adults;18,21,78

the rest were conducted exclusively among infants or children. Nine studies were conducted

among populations that included diverse phenotypes.13-24 The other 8 studies enrolled

populations with homogenous phenotypes including participants with autism,79 congenital

muscular dystrophy,26 epilepsy,27,28 IDD,77,80 neurodevelopmental disorders,25 and peripheral

neuropathy.78


The cost of a WES test reported in studies varied between US$ 1,000 and US

$15,000; trio WES costs more than singleton WES.

In both single-phenotype and diverse phenotype populations, when compared to

standard diagnostic pathways, testing pathways that used WES identified more

diagnoses at a lower cost in some studies, or identified more diagnoses but at a

somewhat higher cost in other studies (range US$ 1,775 to US$ 8,559 higher

depending on where WES was used in the testing pathway).

Pathways with earlier WES testing were more likely to be cost savings than pathways

that used WES later in the testing pathway or as a last resort strategy.

We assessed the certainty of evidence related to all cost outcomes as very low because of study

designs, study limitations, inconsistency, and imprecision.


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ES 4. Discussion

ES 4.1 Summary of the Evidence

WES has a higher diagnostic yield compared to standard testing pathways and phenotype-

specific gene panels. Among all phenotypes, we calculated the pooled diagnostic yield for WES

as 38%, which is higher than the pooled diagnostic yield for traditional testing pathways (21%)

and higher than the pooled diagnostic yield of gene panels (27%). Reanalysis of WES data using

updated variant call algorithms and newly discovered pathogenicity information increases

diagnostic yield on average by about 17%. Because this was a contextual question, we did not

assess the certainty of the evidence.

The findings from the key questions and certainty of evidence is summarized in Figure ES-2.

Figure ES-2. Summary of evidence from whole exome sequencing HTA


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ES 4.2 Limitations of the Evidence Base

The body of evidence on WES has substantial limitations. Most studies were retrospective and

collected data solely from medical records and few studies described protocols for data

abstraction or approaches to ensure standardized, accurate, and replicable abstraction. Some

studies explicitly excluded subjects for which they were unable to obtain outcomes data, which

introduces selection bias. Other studies did not report on how they handled subjects with missing

records or data.

Few studies included a comparison group; therefore, we could only estimate the frequency of

outcomes within a single group. Most studies are small, single-center studies with heterogenous

study populations. As such, it is difficult to compare study outcomes among the studies, and

likely that the results would have been different with a different patient mix. Studies that are not

favorable to WES may not be published. We were unable to evaluate the extent of publication

bias in the body of evidence because these studies are not typically registered in trial registries.

The complexity and rapid evolution of WES further complicates its evaluation. The technology

continues to change rapidly, which hinders the ability to determine the applicability of studies

from just a few years ago. It is also challenging to evaluate how sequencing platforms,

bioinformatics approaches, or testing approaches may affect the findings of individual studies.

The nature of WES testing makes well-designed comparative effectiveness studies complicated.

WES can diagnosis a wide range of conditions—many with very similar phenotypes but very

different underlying genetic diagnoses with drastically different recommended management

strategies and outcomes. Although randomized-controlled trials that use rigorous data collection

and outcome measurement could be designed in order to produce results with a high degree of

certainty under GRADE, they are likely not feasible to conduct in practice.

ES 4.3 Clinical Practice Guidelines and Related Health Technology

Assessments

We did not identify any clinical practice guideline specific to diagnostic testing with WES. We

identified 4 HTAs, 2 were not published in English and 2 were not publicly accessible.81-84

We identified 1 narrative review from the “Model Coverage Policies” page on the American

Academy of Neurology’s (AAN’s) website.85 This document includes suggested indications and

contraindications for exome sequencing, which are detailed in Table 15 of the full report.

We identified 6 documents produced by the ACMG including a policy statement published in

2012 entitled “Points to Consider in the Clinical Application of Genomic Sequencing”; these are

listed in Table ES-1.


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Table ES-1. Indications for diagnostic testing from 2012 policy statement entitled “Points to Consider in the Clinical Application of Genomic Sequencing”86

WGS/WES should be considered in the clinical diagnostic assessment of a phenotypically affected individual when: a. The phenotype of family history data strongly implicate a genetic etiology, but the phenotype does not correspond

with a specific disorder for which a genetic test targeting a specific gene is available on a clinical basis. b. A patient presents with a defined genetic disorder that demonstrates a high degree of genetic heterogeneity,

making WES or WGS analysis of multiple genes simultaneously a more practical approach. c. A patient presents with a likely genetic disorder but specific genetic tests available for that phenotype have failed to

arrive at a diagnosis. d. A fetus with likely genetic disorder in which specific genetic tests, including targeted sequencing test, available for

that phenotype have failed to arrive at a diagnosis. i. Prenatal diagnosis by genomic (i.e., next generation whole exome or whole genome) sequencing

has significant limitations. The current technology does not support short-turnaround times which are often expected in the prenatal setting. There are high false positive, false negative, and variants of unknown clinical significance rates.

Abbreviations: WES = whole exome sequencing; WGS = whole genome sequencing

ES 4.4 Selected Payer Coverage Policies

An overview of selected payer coverage policies for WES is provided in Table ES-2. CMS does

not have a national coverage determination for WES. Five commercial payers cover WES when

beneficiaries have met specific clinical criteria (detailed in Table 17 of the full report).

Table ES-2. Overview of payer coverage policies for whole exome sequencing

Medicare Medicaid Aetna Cigna Humana Kaiser Permanente

Premera Blue Cross

Regence BlueShield Tricare UnitedHealth

— — x x —

Notes: = covered when specific criteria have been met; = not covered; — = no policy identified.

ES 4.5 Limitations of this HTA

This HTA was limited to peer-reviewed studies published in English. Our search was limited to 3

bibliographic databases; however, we conducted extensive hand searches to identify potentially

relevant articles. Because of practical constraints, our key questions focused on clinical utility

outcomes, health outcomes, safety outcomes, and cost outcomes. We did not systematically

review studies of diagnostic yield. However, we provided information about diagnostic yield

based on 4 systematic reviews and 99 primary research studies that we identified as having

relevant diagnostic yield information during full-text screening.

ES 4.6 Ongoing Research

We identified 15 recently completed or ongoing studies that may be relevant to this topic. Most

are single-arm observational cohorts. The only ongoing RCT that we identified is sponsored by

the University of North Carolina at Chapel Hill in collaboration with the National Human

Genome Research Institute and 2 other North Carolina-based health care systems.87 In this RCT,

children and adults with diverse phenotypes are randomized to 1 of 4 study arms 1) previsit

preparation with usual care and exome sequencing, 2) previsit preparation with usual care, 3) no


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previsit prep with exome sequencing, and 4) no previsit prep and usual care. This study plans to

enroll 1,700 participants with an estimated study completion date of May 2021.

ES 5. Conclusion

WES increases the yield of molecular diagnosis over standard diagnostic testing. A diagnosis

from WES changes clinical management for some patients, but our certainty in the estimate of

this frequency is very low. The evidence regarding the impact of WES testing on health and most

safety outcomes is limited, though we have low certainty that the proportion of patients tested

who receive a medically actionable secondary finding is about 3.9%. WES may be cost-effective

in terms of diagnostic success, but our certainty is very low.


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Whole exome sequencing: final evidence report Page 1

Full Technical Report

1. Background

We conducted this health technology assessment (HTA) to assist the State of Washington’s

independent Health Technology Clinical Committee with determining coverage for whole exome

sequencing (WES).

1.1 Condition Description

WES may be indicated for testing for a wide range of genetic diseases. This test is most

commonly used when a patient is suspected of having a genetic disorder, but the signs and

symptoms are not recognizable as a specific genetic condition. This test is also used when the

patient’s phenotype could be consistent with multiple genetic disorders. Except for monozygotic

twins, the genomes of all individuals are different in thousands of places. For convenience in

genetic testing, one designated sequence serves as a reference sequence, and the sequences of

patients who are tested are compared to the reference sequence. A single base pair may be

different (called single nucleotide variant [SNV] or polymorphism [SNP]) or a whole section of a

gene, chromosomal region, or chromosome may be different. These differences, collectively

called genetic variants, make each individual unique. A variant in the sequence of a gene may or

may not affect the gene’s function or result in symptoms. Variants that cause malformation,

dysfunction, or disorders are termed pathogenic variants.

Indications that a symptom or phenotype may be related to a pathogenic genetic variant include,

but are not limited to, dysmorphic features, multiple anomalies, unexplained neurocognitive

impairment, multifocal presentation, earlier or more severe onset of common symptoms, or a

family history of similarly affected individuals.88,89 Although genetic conditions are often

thought of as having onset during infancy or childhood, many genetic disorders first become

symptomatic in adulthood. Some conditions with pediatric onset may not be diagnosed until

adulthood, when their presentation may be confusing.90 Examples of clinical scenarios for which

WES may result in a diagnosis, and the potential diagnoses are:

Siblings with hypotonia, dystonia, oculogyric crises and developmental delay and onset

at 2 months of age. No similarly affected patients in the family. Possible diagnosis: L-

amino acid decarboxylase deficiency or other disease of neurotransmitter synthesis.

These are rare autosomal recessive disorders, which means that both copies of the gene

must include a pathogenic variant.

Adolescent male presents with muscle weakness in his legs and arms that seems to be

worsening. The extended family history is unknown. Possible diagnosis: any of over 20

muscular dystrophy types and subtypes, myopathy, or Pompe disease.

Twenty-nine year-old woman presents with endometrial cancer. Family history

includes multiple individuals diagnosed with different cancers, including multiple


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causes of colorectal cancer, before the age of 40. Possible diagnosis: Lynch syndrome,

Li-Fraumeni syndrome, other inherited cancer syndromes.

Two female siblings with congenital heart defect and neural tube defect, dysmorphic

features, craniofacial abnormalities, cataracts and developmental retardation. Normal

metabolic and chromosomal testing. Potential diagnoses: Meckel-Gruber syndrome,

Roberts syndrome, Walker-Warburg syndrome. Testing for microdeletion of 22q11 was

normal.

Because WES sequences the entire exome, it may also identify genetic disorders other than those

that cause the patient's phenotype, some of which require specific medical management. For

example, identification of a pathogenic mutation in the BRCA2 gene would prompt early,

frequent screening for breast cancer or other preventive measures.1 Initially, such mutations were

referred to as incidental findings, until evidence emerged that such findings were common for

WES or whole genome sequencing (WGS). They are now referred to as secondary findings.

Depending on the patient's phenotype and resulting variant filtering, described in Section 1.3

below, such variants may not be identified unless they are specifically sought. In 2013, the

American College of Medical Genetics and Genomics (ACMG) recommended specifically

looking for pathogenic and likely pathogenic variants in 56 genes for which medical

management guidelines were available in order to standardize the reporting of secondary

findings.2 Three additional genes for which medical management guidelines had become

available were added to the list in 2016.91

1.2 Disease Burden

There are more than 6,000 human genetic diseases.3 Although genetic diseases are individually

rare, they collectively affect approximately 1 in 17 individuals.4 One study estimated that the

range of total inpatient charges for U.S. pediatric patients related to suspected genetic diseases in

2012 was US$ 14 to US$ 57 billion—11% to 46% of all pediatric inpatient charges.5

1.3 Technology Description

WES identifies the DNA base-pair sequence of the protein-coding regions of the genome,

including proximal regulatory segments and splicing junctions.6 WES is primarily used to

identify small changes in base-pair sequences that disrupt protein function and cause disease;

however, improved bioinformatics has increased the ability to identify chromosomal copy

number variants (i.e., larger deletions or duplications involving larger stretches of DNA) from

sequenced data and changes in the mitochondrial genome. WES may be performed for both

clinical and research purposes. Diagnostic WES testing is ordered by a physician or other health

care professional and is conducted in a clinical diagnostic laboratory to aid in the diagnosis of a

patient. Parents’ (Trio WES) or siblings’ genes may also be sequenced to help interpret identified

variants. Research WES testing is used to identify novel gene variants, further characterize a

common disease gene or genes among multiple families or patients with the same diagnosis or

evaluate alternative strategies for conducting WES testing.


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WES uses next-generation sequencing (NGS) technologies; NGS makes many copies of the

target genome, cuts them into random sequences, and simultaneously sequences the resulting

fragments. After this sequencing step, WES requires a series of bioinformatics analyses to

interpret the sequencing. The stages of the bioinformatics analyses, often referred to as the

analysis pipeline, are as follows:92

1. Segment-sequence generation: Bioinformatic algorithms that are provided by the

manufacturer of the sequencer convert the raw data generated by the sequencing machine

into strings of nucleotide bases (i.e., As, Cs, Ts, and Gs). In addition to the read

sequences, these algorithms provide quality metrics for each base call that describe the

likelihood that the call is correct.

2. Genome-sequence generation: In this step, bioinformatics software aligns the sequence

segments to a reference genome. The Genome Reference Consortium produces the

reference sequences,93 which are periodically updated. The laboratory conducting the

genome-sequence generation should specify the reference genome version it used in the

laboratory report.

3. Variant identification: Statistical models identify the differences between a patient’s

exome and the reference genome. This process is complex and may require multiple

algorithms to identify (i.e., call) different types of variants. The accuracy of calling

variants differs by variant type, variant characteristics, and the details of the sequencing

method. WES identifies single-nucleotide changes with high accuracy (> 99.5%

sensitivity and specificity). Insertions and deletions are harder to call accurately, and—

somewhat counterintuitively—the larger the insertion or deletion, the harder it is to

identify. The details of the sequencing analysis determine if it is possible to identify large

regions of homozygosity or the patient’s genotype at a specific locus.

4. Genome interpretation: This analysis places the identified variants into the larger

genomic and clinical context needed to interpret the variant. Information is extracted

from bioinformatic databases to identify the gene in which the variant occurs and its

function, the effect of the variant on the gene transcript, and the Human Genome

Variation Society nomenclature of the variant. It would be impossible to manually review

all the variants in an exome; therefore, bioinformatics algorithms filter and prioritize

variants that are more likely to be pathogenic, which require a further, manually driven

review. Algorithms filter out variants that are common in the population frequency or that

do not change protein function, and gene variants that are either irrelevant to the

phenotype or not expressed in the affected tissue. An example filtering process is

depicted in Figure 1. If parent or sibling exomes are available, algorithms filter out

variants present in unaffected relatives and prioritize those shared with affected relatives.


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Figure 1. Variant filtering process

5. Variant interpretation: The final step of the analysis is to develop a full interpretation

of the identified variants. This step is manually driven, although it uses multiple

bioinformatic tools and databases. The laboratory may apply additional prioritization

tools to make the number of variants interpreted feasible. Only variants that may be

relevant to the patient’s clinical condition or variants determined by the ACMG to be

medically actionable variants are included in the clinical report that is returned to the

ordering clinician and patient. These variants are classified as pathogenic, likely

pathogenic, variants of unknown significance, likely benign, or benign. Pathogenic

variants may be confirmed by traditional Sanger sequencing, which uses enzymes to cut

DNA into segments based on specific sequences then sequences the resulting segments.

6. Reporting: A clinical laboratory report for WES usually includes as primary findings all

variants identified in genes associated with the clinical phenotype of the patient and their

interpretation and ACMG-defined medically actionable variants as secondary findings.

Some laboratories report additional secondary findings, including whether the patient is a

carrier for any autosomal-recessive disorders and drug metabolism variants that affect the

use of certain drugs.94 Which findings are reported to providers and patients depends on

what the patient consents to receiving.

1.3.1 Use in Clinical Practice

The use of WES within clinical practice is still evolving in terms of how and where it is used

within a diagnostic testing pathway for individual patients. Typically, WES is used when a

monogenic disorder is suspected but when the patient’s phenotype does not suggest a specific


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disorder for testing. WES can replace most single and multigene panel testing, but up until

recently could not replace chromosomal microarray analysis (CMA) for the detection of copy

number variants.

1.4 Regulatory Status

The U.S. Food and Drug Administration (FDA) approves sequencing platforms when they are

sold to conduct clinical NGS, including WES. FDA approval is based on the demonstration of

analytic validity, in other words that the sequencing machines correctly sequence DNA

specimens. The FDA does not regulate WES as a diagnostic test, which involves both the

sequencing component and the bioinformatics and variant interpretation component. WES is

conducted by laboratories that are accredited by the Clinical and Laboratory Improvement Act

(CLIA) to conduct high complexity testing. Because of the equipment and software involved

(particularly for the bioinformatics platform), this test is generally only conducted in laboratories

associated with large, tertiary medical centers or commercial genetics laboratories.

1.5 Policy Context

The State of Washington HCA selected this topic for review because of high concerns for safety

and medium concerns for efficacy and cost.

1.6 Washington State Agency Utilization Data

The WES utilization analysis conducted by the State of Washington HCA combined member

utilization and cost data from the following Washington agencies: Medicaid Managed Care

(MCO) and Medicaid Fee-for-Service (FFS). The Department of Labor and Industries (LNI)

Workers’ Compensation Plan reported no WES utilization. The Public Employees Benefit Board

Uniform Medical Plan (PEBB/UMP) reported less than the minimum number of individuals

necessary to safely release agency-by-agency findings and still protect patient confidentiality.

Based on CPT codes for WES (i.e., 81415, 81416, and 81417), claims for 390 tests have been

paid since 2015; nearly half were paid in 2018. The average cost per professional claim was

$12,530 in 2015 and $888 in 2018. Additional details are provided in Appendix A.


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2. Methods

This section describes the methods we used to conduct this HTA.

2.1 Research Questions and Analytic Framework

We developed the following research questions to guide the systematic evidence review of

primary research studies:

Key Question 1: Effectiveness (Clinical Utility)

1a. In what proportion of patients does testing with WES result in a clinically actionable finding

(i.e., the diagnosis resulting from WES leads to something that can be treated, prevented, or

mitigated)?

1b. In what proportion of patients does testing with WES result in an actual change to the

patient’s medical management (medication or therapies, follow-up testing, medical monitoring)

or genetic counseling (reproductive risks or risks of other family members)?

1c. What is the effect of testing pathways that include WES on medical management or genetic

risk counseling compared to testing pathways that do not include WES?

Key Question 2: Effectiveness (Health Outcomes)

2a.: What are the health outcomes, including mortality, among patients who have WES testing?

2b: What are the health outcomes, including mortality, of patients who receive testing pathways

that include WES compared to alternative testing pathways with or without WES?

Key Question 3: Safety and Harms

3a: How many patients receive erroneous results after WES testing, either false positive or false

negative results? What harms are caused by these test results and how many patients experience

these harms?

3b: What harms are caused by uncertain WES results or a lack of diagnosis after WES testing?

3c: How many patients receive reports on ACMG-defined medically actionable variants after

WES testing? What harms do they experience, and how many patients experience these harms?

3d: How frequently do WES results cause harm to family relationships?

Key Question 4: Cost

4a: What is the cost of WES testing?

4b. What is the cost per diagnosis of pathways that include WES testing?


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4c: What is the cost per additional diagnosis, comparing a pathway with WES to an alternative

pathway with or without WES?

4d: What is the cost-effectiveness of testing with WES?

We also created the analytic framework, shown in Figure 2, to guide our review.

Figure 2. Analytic framework for HTA on whole exome sequencing

Abbreviations: CMA=chromosomal microarray; ECG=electrocardiography; EEG=electroencephalography;

EMG=electromyography; KQ=key question; WES=whole exome sequencing

In addition, we addressed the following contextual questions, which were not systematically

reviewed and therefore are not shown in the analytic framework.

Contextual Question 1: What is the diagnostic yield of WES either alone or as part of a testing

pathway and what are the factors (e.g., phenotypes being tested, testing platforms and

bioinformatics analysis used) that contribute to variation in diagnostic yields?

Contextual Question 2: How often does WES return variants of uncertain clinical significance

and what impact does repeat bioinformatics analysis have on diagnostic yield?

The State of Washington HTA Program posted a draft of these research questions with study

selection criteria for public comment from March 19, 2019 to March 28, 2019. The final key

questions and response to public comments on the draft key questions were published on June

17, 2019 and are available at the Program’s website.95

Persons with clinical signs/

symptoms suspected of

having a genetic condition

Single gene testing

Multi-gene panel

WES



WES

WES

WESCMA

Alone or in combination:CMA, single

gene testing, multi-gene

panel

History, exam, non-genetic

testing (laboratory,

imaging, EEG, ECG, EMG)

WES Repeat WES

Health Outcomes

Diagnosis

Clinical Utility/

Changes in Management

KQ 1

KQ 2

KQ 3

HarmsFalse Positives, False Negatives

Incidental FindingsPsychological Impact of No Diagnosis, Uncertain

Results, Unexpected Information

CostsCost per diagnosis

Cost per additional diagnosisCost-effectiveness

KQ 4

No genetic testing

Comparator Path 1

Path A

Path B

Path C

Path D

Path E

Comparator Path 2


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2.2 Data Sources and Searches

We searched MEDLINE® (via PubMed), Embase, and a clinical trials registry

(www.clinicaltrials.gov) for relevant studies published in English from inception to March 14,

2019, and actively surveilled the published literature through August 31, 2019. In brief, we used

medical subject headings (MeSH terms) and text words associated with the WES. We limited the

search by eliminating studies indexed using terms for bacteria, viruses, and animals. We used

MeSH terms to remove editorials, letters, and publication types that did not represent primary

research studies from the search yield. We conducted targeted searches of the Centers for

Medicare & Medicaid Services (CMS) and FDA websites, selected payer and health care

professional society websites, and websites of other organizations that conduct and disseminate

HTAs or clinical practice guidelines. The detailed electronic search strategy is presented in

Appendix B. In addition, we reviewed the reference lists of relevant studies, systematic reviews,

practice guidelines, and other HTAs on this topic to identify any relevant primary research

studies that were not found through the electronic search.

2.3 Study Selection

Table 1 summarizes the study selection criteria related to the populations, interventions,

comparators, outcomes, study time periods, study design, and settings that defined the scope of

this HTA, which are further described in the sections that follow. Two review team members

independently screened titles, abstracts, and full-text articles based on these study selection

criteria. Discrepancies in study selection at the full-text level were adjudicated by the lead

investigator, or in some cases, by team consensus.

Table 1. Population, intervention, comparator, outcomes, period, setting, and other study-selection criteria for HTA on WES

Domain Included Excluded

Population Children or adults, with or without a clinical diagnosis, suspected of having a genetic disease

Embryos and fetuses

Patients with nonsyndromic cancer or infections, where WES is being used to characterize the tumor or microbe

Deceased persons

Intervention Diagnostic WES alone (Path A in Figure 2) or as part of a sequential testing pathway after clinical, laboratory and imaging evaluation (Paths B, C, D in Figure 2)

Reanalysis of diagnostic WES findings at a later interval (Path E in Figure 2)

Single-gene sequencing (traditional Sanger sequencing or next-generation sequencing)

Multigene panels (traditional Sanger sequencing or next-generation sequencing)

Whole mitochondrial sequencing

WES to identify acquired mutations in tumors

WES of infectious agents

Genome-wide association studies

Research-based WES (i.e., studies focused on elucidating the biology or underlying genetics of a disorder)

WES when focused on evaluating alternative methods for sequencing or variant calling

WES when focused exclusively on identifying copy number variants

Whole genome sequencing

http://www.clinicaltrials.gov/


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Table 1. Population, intervention, comparator, outcome, timing, setting and other study selection criteria for HTA on WES (continued)


Comparator Clinical, laboratory, or imaging evaluation with no genetic testing (Comparator Path 1 in Figure 2)

Testing pathways that use only CMA, single-gene testing, or multigene panels (Comparator Path 2 in Figure 2). Single-gene testing and multigene panels can be performed by traditional Sanger sequencing or with next-generation sequencing.

Testing pathways that use WES in sequence with other testing, and including WES reanalysis (Paths B, C, D, and E in Figure 2).

Whole genome sequencing

Outcomes Clinical utility o Results from WES could be or are used for

medical management (e.g. therapy, further diagnostic testing, monitoring), reproductive counseling, or risk counseling for other family members

Health outcomes o Mortality, length of survival o Morbidity, cognitive ability, functional

outcomes

Safety o Misdiagnosis (false positives, false

negatives) o Proportion of patients with ACMG-defined

medically actionable variants o Psychosocial harms (e.g., anxiety, family

stress, depression, distress, financial consequences) to proband and family from testing related to lack of diagnosis, uncertain findings, incidental findings, and unexpected information (e.g., carrier status, non-paternity)

o Employment or insurance discrimination

Costs o Cost of testing (U.S. based studies from

previous 2 years only) o Cost per diagnosis o Cost per additional diagnosis o Cost-effectiveness

Outcome differences due only to different genetic defects

Clinical utility and health outcomes related to incidental (e.g., secondary) findings

Cost of testing from studies performed in non-U.S. countries if this was the only cost outcome provided

Cost of testing from studies performed in the U.S. but that are older than 2 years if this was the only cost outcome provided


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Table 1. Population, intervention, comparator, outcome, timing, setting and other study selection criteria for HTA on WES (continued)


Setting Any outpatient or inpatient clinical setting in countries categorized as ‘very high’ on the UN 2017 Human Development Indexa

Non-clinical settings, countries categorized other than ‘very high’ on the 2017 UN Human Development Indexa

Study Design

and Risk of

Bias Rating

Study designs96

Clinical trial (single group or controlled)

Cohort (single group of more than 10 participants or families or controlled)

Case-control

Cross-sectional

Case series (between 5 to 10 participants or families)

Cost analyses, cost-benefit analysis, cost-utility analysis, cost-effectiveness analysis

Modeling studies (for clinical utility, health outcomes, and cost outcomes only)

Qualitative study designs (for safety outcomes only)

Risk of Bias Rating

Any

Case reports (fewer than 5 participants)

Narrative reviews

Editorials and commentary

Letters to the editor

Systematic reviews were not included but were hand searched to identify relevant primary research studies

Language and

Time Period

English

2010 or later

Any language other than English

Studies published prior to 2010

Notes: a Countries categorized as “very high”:97 Andorra, Argentina, Australia, Austria, Bahrain, Belgium, Brunei Darussalam,

Canada, Chile, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong China

(SAR), Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea (Republic of), Kuwait, Latvia, Liechtenstein, Lithuania,

Luxembourg, Malta, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Qatar, Romania, Russian

Federation, Saudi Arabia, Singapore, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Arab Emirates, United

Kingdom, United States.

Abbreviations: ACMG =American College of Medical Genetics and Genomics; CMA = chromosomal microarray; HTA

=Health Technology Assessment; NGS =next-generation sequencing; U.N. = United Nations; U.S. =United States; WES = whole

exome sequencing; WGS = whole genome sequencing

2.3.1 Population

Studies were selected if they enrolled children or adults suspected of having a genetic disease.

We excluded studies focused primarily on suspected genetic disease in deceased persons,

embryos, or fetuses.

2.3.2 Intervention and Comparator

We selected studies that used WES in a clinical diagnostic context, either alone or as part of a

testing strategy that included other clinical laboratory, imaging, or other diagnostic evaluations.

We excluded studies that used WES to (1) characterize tumors or infectious microbes, (2)

sequence the whole mitochondrial genome, (3) conduct research for elucidating possible

underlying genetics of a disorder or to identify novel variants, (4) assess different


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methodological approaches to conducting a WES analysis, and (5) exclusively characterize copy

number variants. We also did not select studies that only reported on WGS, genome-wide

association studies, or were focused primarily on single-gene or multigene panel testing. Eligible

comparator testing strategies included (1) standard clinical diagnostic testing without genetic

testing; (2) usual testing with CMA or single- or multigene panel testing; or (3) usual care with

CMA, single- or multigene testing, and WES but in different sequences, including WES

reanalysis. However, we did not require studies to have a comparator testing strategy.

2.3.3 Outcomes

For the efficacy research question on clinical utility, we selected studies that reported on results

from WES that either potentially could be used or had been used for medical management,

further diagnostic testing or monitoring, or risk counseling for other family members, including

reproductive risk counseling.

For the efficacy research question on health outcomes, we selected studies that reported on

mortality, length of survival, morbidity, cognitive ability, and functional outcomes.

For the safety research question, we selected studies that reported misdiagnosis (i.e., false-

positives, false-negatives), proportion of patients with ACMG-defined medically actionable

variants (i.e., secondary or incidental findings), psychosocial harms, and employment or

insurance discrimination.

For the cost research question, we selected studies that reported on the costs of the WES test,

cost per patient of testing, cost per diagnosis of testing, and cost-effectiveness outcomes. We did

not select studies in which the only eligible outcome was the cost of the WES test unless the

study was conducted in the U.S. within the previous 2 years.

2.3.4 Settings

Studies conducted in any inpatient or outpatient clinical setting were eligible for selection.

Studies that were conducted in countries with a development rating designated as very high by

the United Nations Human Development Index in 2017 were eligible for selection because these

countries (e.g., Canada, Europe, Australia, New Zealand, Japan, S. Korea, Singapore, Hong

Kong) and others are like the U.S. with respect to standards of medical practice.97 We excluded

studies conducted in countries with a development rating designated as less than very high.

2.3.5 Study Design

We selected studies that used any of the following study designs: clinical trials, single or

controlled cohorts (10 or more participants or families), case-control studies, cross-sectional

studies, case series studies (between 5 to 10 participants or families), cost analyses, cost-benefit

analyses, cost-utility analyses, cost-effectiveness analyses, modeling studies, and qualitative

research studies (for safety outcomes only). Case reports, editorials, comments, letters, and

narrative reviews were not eligible for selection. We also did not include systematic reviews, but

we did hand search them to identify relevant primary research studies that may have been missed

by our search.


Final


2.3.6 Time Period

We selected studies published in 2010 or later because WES had not been used for clinical

purposes prior to this date.

2.3.7 What Is Excluded From This HTA

This review did not include studies published in languages other than English or conducted in

countries that are not very highly developed based on the United Nations Human Development

Index.97 For the key questions in this review, we did not include studies only reporting the

diagnostic yield of WES.

2.4 Data Abstraction and Risk of Bias Assessment

One team member extracted relevant study data into a structured abstraction form, and a senior

investigator checked those data for accuracy.

Two team members conducted independent risk of bias assessments on all included studies;

discrepancies were resolved by discussion. Because most study designs were single-arm

observational cohort studies and because existing instruments for diagnostic test accuracy studies

were not well suited for the assessment of this body of literature (i.e., studies were not comparing

an index test to a reference test), we developed a structured form to assess risk of bias for clinical

utility, health outcomes, and safety outcomes. The form included signaling questions to assess

the main domains of bias including selection, performance, missing data, and outcome

measurement. Risk of bias was assessed as high, some concerns, or low for each separate

outcome domain (clinical utility, health outcomes, and safety outcomes). We used the Quality of

Health Economic Studies Instrument to assess the risk of bias of included cost analyses.7 We

considered studies with scores on this instrument of 90 or above to have low risk of bias, studies

with scores between 60 and 89 to have some concerns for bias, and studies with scores below 60

to have high risk of bias.

2.5 Data Synthesis and Quality of Evidence Rating

We qualitatively synthesized study characteristics and results for each research question in

tabular and narrative formats. We used Stata (version 15) to conduct quantitative pooling of the

diagnostic yield estimate for contextual question 1. We were not able to conduct quantitative

syntheses for any of the key questions because of the clinical and methodological heterogeneity

in this evidence base.

We graded the certainty of the evidence for each outcome using the Grading of

Recommendations Assessment, Development, and Evaluation (GRADE) approach.8 With

GRADE, the certainty of evidence can be graded as very low, low, moderate, or high based on

imprecision, inconsistency, and study limitations. Table 2 defines these levels.98 Bodies of

observational evidence begin with a low certainty rating and can be downgraded for imprecision,

inconsistency and study limitations. Bodies of evidence can also be upgraded from low for other

considerations (e.g., large effect, evidence of dose-response). We note here that the GRADE

framework was initially developed for RCTs of interventions; it may not be well suited for

assessing the strength of evidence for genetic testing.


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Table 2. Certainty of evidence grades and definitions98

GRADE Definition

High We are very confident that the estimate of effect lies close to the true effect for this outcome. The body of evidence has few or no deficiencies. We believe that the findings are stable, that is, another study would not change the conclusions.

Moderate We are moderately confident that the estimate of effect lies close to the true effect for this outcome. The body of evidence has some deficiencies. We believe that the findings are likely to be stable, but some doubt remains.

Low We have limited confidence that the estimate of effect lies close to the true effect for this outcome. The body of evidence has major or numerous deficiencies (or both). We believe that additional evidence is needed before concluding either that the findings are stable or that the estimate of effect is close to the true effect.

Very Low We have very limited confidence that the estimate of effect lies close to the true effect for this outcome. The body of evidence has numerous major deficiencies. We believe that substantial additional evidence is needed before concluding either that the findings are stable or that the estimate of effect is close to the true effect.

3. Results

3.1 Literature Search

Figure 3 depicts the study flow diagram. We screened 5,567 unique citations. We excluded

5,136 citations after title and abstract review. We dually reviewed 431 full-text articles and

included a total of 57 studies reported in 60 articles published between 2014 and 2019. Thirty

studies provided evidence on clinical utility (KQ1), 7 studies provided evidence on health

outcomes (KQ2), 26 studies provided evidence on safety outcomes (KQ3), and 17 studies

provided evidence on cost outcomes (KQ4). Individual study and population characteristics and

findings are summarized in Appendix C. The list of articles we screened at the full-text stage, but

which we excluded, is provided in Appendix D. Note that articles may have been excluded for

more than one reason, but we report only one reason. We report our individual study risk of bias

assessments for included studies in Appendix E.

The rest of the results section is organized as follows. First, we describe findings related to the 2

contextual questions. Next we describe findings from key questions. This includes findings

related to clinical utility (Section 3.3), health outcomes (Section 3.4), safety outcomes (Section

3.5), and cost outcomes (Section 3.6).


Final


Figure 3. Study flow diagram for HTA on whole exome sequencing

Number of records

identified through

database searches:

5387

Number of records

identified through

database searches:

5387

Number of titles/abstracts screened after duplicates

removed:

5567

Number of titles/abstracts screened after duplicates

removed:

5567

Number of titles/abstracts

excluded:

5136

Number of titles/abstracts

excluded:

5136

Number of full-text publications excluded:

371

By reason:

Ineligible publication type/study design: 133

Ineligible country: 19

Ineligible population: 15

Ineligible test: 60

Ineligible outcome: 93

Not in English: 4

Duplicate or superseded: 9

Protocol or in Process Study: 29

Systematic reviews used to identify

relevant studies: 7

Not retrievable: 2

Number of full-text publications excluded:

371

By reason:

Ineligible publication type/study design: 133

Ineligible country: 19

Ineligible population: 15

Ineligible test: 60

Ineligible outcome: 93

Not in English: 4

Duplicate or superseded: 9

Protocol or in Process Study: 29

Systematic reviews used to identify

relevant studies: 7

Not retrievable: 2

KQ1 (Clinical Utility):

30 studies

33 publications

KQ1 (Clinical Utility):

30 studies

33 publications

Number of full-text publications assessed for

eligibility:

431

Number of full-text publications assessed for

eligibility:

431

Number of additional citations identified

through other sources (e.g., targeted

CPG search, hand search):

221

Number of additional citations identified

through other sources (e.g., targeted

CPG search, hand search):

221

57 studies

60 publications

57 studies

60 publications

Duplicates:

41

Duplicates:

41

KQ2 (Health):

7 studies

7 publications

KQ2 (Health):

7 studies

7 publications

KQ3 (Safety):

26 studies

26 publications

KQ3 (Safety):

26 studies

26 publications

KQ4 (Cost):

17 studies

20 publications

KQ4 (Cost):

17 studies

20 publications

Abbreviations: CPG = Clinical Practice Guidelines; KQ = key question


Final


3.2 Contextual Questions

3.2.1 Contextual Question 1: Overall diagnostic yield

Four systematic reviews9-12 and 99 individual studies (see Appendix F) provided information on

the diagnostic yield of WES. Three of the systematic reviews included articles on diverse

phenotypes (Table 3).9,11,12 The fourth review was limited to studies of epilepsy.10 The degree of

diagnostic testing prior to WES testing that was received by participants enrolled in these studies

varied; most all had received some initial diagnostic evaluation (specialty consultation,

laboratory, imaging). Many had also received some genetic testing (e.g., single or multigene

panels, CMA).

Two reviews provided pooled estimates of diagnostic yield. Among children with any phenotype

suspected to be of genetic origin, 36% were diagnosed by WES, 8% by CMA, and 41% by

WGS.11 Among patients of any age who presented with epilepsy, 45% were diagnosed by WES,

8% by CMA and 23% by epilepsy-specific gene panels. Two studies only reported the range

across studies or individual study estimates. Schwarze et al. reported a range of 3% to 79%,9 and

Alam et al. reported a range of 16% to 79%.12 The lowest estimate (3%) was for patients with

colorectal cancer, of which approximately 5% of cases are due to a single gene disorder.9 The

highest estimate, 79%, was among patients with childhood-onset muscle disorders.9

Table 3. Systematic reviews of the diagnostic yield of WES

Abbreviations: CMA = chromosomal microarray analysis; WES = whole exome sequencing; WGS = whole genome

sequencing; NR = not reported

We also analyzed diagnostic yield data from 99 individual studies (see Appendix F), of which 19

were included in 1 or more of the above noted systematic reviews. Sixty-one of the studies were

published in 2017 or later. Among these 99 studies, we calculated the pooled diagnostic yield for

Author (Year) Inclusion Criteria

Number of Studies; Total Patients

Diagnostic Yield

Schwarze (2018)9 Published 2005 to2016

WES or WGS

Any age group or phenotype

Studied cost (main focus), clinical utility, diagnostic yield or health outcomes

WES: 27; NR WGS: 3; NR

Range: 3% (colorectal cancer to 79% (childhood-onset muscle disorders)

Sanchez Fernández (2019)10

Any publication date

WES, CMA, Epilepsy panel (EP)

Any age group

Phenotype of epilepsy

Any genetic test: 20, NR WES: 6; 1,193 CMA: 8; 2,341 EP: 9; 2,341

Pooled estimates: WES: 45% (95% CI, 33% to 57%) CMA: 8% (95% CI, 6% to 12%) EP: 23% (95% CI, 18% to 29%)

Clark (2018)11 Published in 2011 to2017

WES, WGS or CMA

Children

Any phenotype

Studied diagnostic yield

WES: 26; 9,014 CMA: 13; 1,1429 WGS: 7; 374

Pooled estimates (severe heterogeneity): WES:36% (95% CI, 33% to 40%) CMA: 10% (95% CI, 8% to 12%) WGS: 41% (95% CI, 34% to 48%)

Alam (2018)12 Published in 2010 to2017

WES

Children

Any phenotype

Studied cost

WES: 11, NR Range: 16% to 79%


Final


WES as 38% (95% CI, 35.7% to 40.6%). In comparison, we calculated the pooled diagnostic

yield of traditional testing pathways (4 studies16,20,26,27) as 21% (95% CI, 5.6% to 36.4%) and the

diagnostic yield of gene panels (6 studies26,28-32) as 27% (95% CI, 13.7% to 40.5%).

One study conducted diagnostic WES in patients also undergoing traditional testing chosen by

the patient’s physician and compared diagnostic rates using the two testing strategies.20 Thirty-

six (24%) patients who received a diagnosis from WES did not receive a diagnosis from

traditional testing.

3.2.2 Patient Characteristics That Affected Diagnostic Yield

The likelihood of a genetic cause and, therefore, the diagnostic yield of WES varied by patient

age and phenotype. The diagnostic yield decreased as the age of participants increased: 42%

among infants, 38% among children, and 20% among adults. There were 7 disorders or groups of

related phenotypes for which there were more than 2 studies of diagnostic yield (see Table 4).

The diagnostic yield for these disorders ranged from 29% for participants with an intellectual or

developmental disability to 48% for those with limb-girdle muscular dystrophy.

Table 4. Diagnostic yield for specific disorders

3.2.3 Contextual Question 2: Reanalysis and Diagnostic Yield

Reanalysis of WES data using updated variant call algorithms and newly discovered

pathogenicity information increases diagnostic yield. Among the 8 studies that examined the

yield from reanalysis, on average 17% of previously undiagnosed patients were diagnosed by

reanalysis of existing WES data.13,21,33-38 One study compared reanalysis of existing WES data to

performing WGS for patients with previous negative results.36 This study found that 7 of 112

patients had a variant detected by WGS that was not detected by WES after reanalysis.

Reanalysis may also reclassify variants previously thought to be pathogenic to benign, resulting

in some previously diagnosed patients no longer having a genetic diagnosis. The single study

that examined the frequency of such reclassification reported that 39 of 328 (12%) likely

diagnoses of patients with developmental disorders had been retracted since their initial report in

2014.99 However, 23 of the 39 (59%) would not have been considered as likely pathogenic under

the laboratories 2018 guidelines for considering a variant pathogenic.

Phenotype Number of Studies Total Patients Pooled Diagnostic

Yield (%)

Epilepsy 8 598 40

Intellectual or Developmental Disability 7 2,737 29

Neurologic Disorders 7 434 33

Neurodevelopmental Disorders 5 709 28

Limb-girdle Muscular Dystrophy 4 262 48

Peripheral Neuropathy 4 152 32

Undiagnosed After Standard Workup 4 809 31


Final


3.2.4 Analytic Validity

Although this HTA included the number of false-positives and false-negatives as eligible safety

outcomes, studies defined by the scope of the key research questions for this HTA did not

generally report analytic validity. Thus, we provide additional contextual information to

supplement the findings for those outcomes described in Section 3.4.

Analytic errors in next-generation sequencing can be due to sequencing quality or to the

bioinformatics algorithms used to identify sequence variants. A 2014 study examined genotype

discordance between multiple sequencing runs by sequencing platform (Illumina versus

Complete Genomics), sequencing coverage, type of specimen (blood versus saliva), and WES vs.

WGS.100 Error rates were in the range of 1 per 200 to 1 per 500 single-nucleotide variants

(SNVs) overall, and 4% to 6% for rare variants. False positive rates were much more common

than false negative rates. The estimated error in rare variants is slightly higher than the reported

discordance between WES and Sanger sequencing of 3%.101 Of note, these studies were

published in 2014 and 2015. It is likely that base calling algorithms have improved since these

publications. Lower sequencing coverage resulted in lower discordance but fewer SNVs were

called with high confidence. Specimen type did not affect error rates.

3.3 Key Question 1: Effectiveness (Clinical Utility)

Thirty studies (in 33 publications) reported on clinical utility outcomes.13-16,18,19,22,24,25,27,28,32,34,39-

58 Detailed information regarding study characteristics and outcomes are reported in Appendix C,

Table C-1 and C-2. The key findings are:

Among studies that enrolled patients with diverse phenotypes (18 studies):



o A WES diagnosis resulted in counseling and genetic testing for family members for

between 4% and 97%

Among studies that enrolled patients with epilepsy (5 studies):



Among studies that enrolled patients with a single phenotype (7 studies), all reported

some changes in clinical management following a WES diagnosis, but the data was too

heterogenous to synthesize into a single range.

We assessed the certainty of evidence related to all clinical utility outcomes as very low because

of study designs, study limitations, inconsistency, and imprecision. Detailed certainty ratings are

in Appendix G.


Final


3.3.1 Study and Population Characteristics

The included publications were published between 2014 and 2019. The WES testing was

performed during the years 2011 to 2018 as reported in 20 studies. One study was a case series,53

one study was a qualitative study, and one was a controlled observational cohort study;22 all

other studies were single-arm observational cohorts. The controlled observational cohort study

compared a rapid WES protocol to a standard WES protocol.22 Fourteen studies were rated as

having a high risk of bias,13,25,27,28,32,34,39,42,45,51,53,55-57 and 15 as having some risk of

bias.18,19,22,24,40,41,43,44,46-49,52,54,58 Risk of bias was not assessed for the qualitative study.50

Sixteen of the included studies were conducted in the U.S.,24,25,39-41,43,45-50,54,56-58 6 were

conducted in Australia,13-16,19,22,27,28,34 and 2 each were conducted in Canada44,55 and

Germany.51,52 One study was conducted in Argentina,18 France,42 Israel,53 and The Netherlands,32

respectively. Three studies were industry-sponsored,39,45,50 7 studies had some industry

funding,13-16,19,22,25,27,44,50,53 and 12 studies reported no industry funding.18,34,40-43,47,48,50,51,54,55 For

the remaining 8 studies, it was unclear whether or not any industry funding was

involved.24,28,32,46,49,52,57,58

The number of probands who underwent WES in each study ranged from 6 to 278 (32% to 68%

were female among those reporting). One study was conducted among 62 health care providers

who had referred patients for clinical WES.39 The median age of patients ranged from 26 days to

66 years among those studies reporting. Enrollment in 29 studies was limited by age group: 3

studies28,40,56 only included infants, 13 only included children, and 150 only included adults. The

remaining 12 studies included both adults and children.18,32,34,39,41-46,48,57 Seven studies reported

on the ethnicity of participants, which ranged from 55% to 98% European.32,43,44,46,49,52,54

Eighteen of the included studies performed WES on patients with diverse phenotypes. The

remaining studies enrolled single-phenotype participants. Five studies included patients with

epilepsy,27,28,32,34,58 and 7 studies included patients with another phenotype: familial

hypercholesterolemia,50 intellectual developmental disorder,44 malignant infantile

osteopetrosis,53 kidney transplant,48 young onset nephrolithiasis,41 neurodevelopmental

disorders,25 and short stature.51 Ten studies performed singleton WES,13-16,19,22,28,34,48,50,53-55 and 5

performed trio WES.25,27,45,47,56 Five studies reported using a combination of singleton, duo, or

trio WES,18,32,40,46,51 and an additional 6 studies included family members, either affected or

unaffected, outside the parent-proband trio.24,41,43,44,49,52 Four studies did not specify which

family members were sequenced.39,42,57,58

3.3.2 Findings From Studies Among Diverse Populations

Characteristics and outcomes from the 20 studies that enrolled a diverse array of phenotypes are

reported in Table 5. All 20 reported on actual changes in management as a result of WES testing

among their participants. The percent whose medical management changed after receiving a

molecular genetic diagnosis from WES ranged from 12% to 100%. Of the 11 studies that

specifically reported starting, stopping, or changing the dosage of a medication, the percentage

ranged from 5% to 25% of those receiving a diagnosis from WES.16,18,22,39,40,45-47,52,55,57 In 2

studies40,45 13%40 and 14%45 of WES-diagnosed patients received a specific diet

recommendation. Between 5% and 54% of those diagnosed using WES were referred for


Final


additional surveillance or medical specialties in the 6 studies that reported this.16,18,22,47,52,57

Among patients diagnosed with rapid WES, 5% of patients in one study22 and 30% in a second

study56 were redirected to palliative care based on the diagnosis.


Final


Table 5. Summary of characteristics and outcomes for studies evaluating clinical utility among studies enrolling diverse populations

Author (Year);

Country

Risk of

Bias Study Population and Setting

Testing Strategies

Used

Diagnostic

Yield Changes in Management

Results Leading to

Additional Genetic

Counseling or Testing in

Family

Balridge (2017)43;

U.S.

Some 155 adults and children with diverse

phenotypes seen at a university exome

clinic

Trio WES (n=128),

parent, sibling proband

WES (n=1), parent,

sibling WES (n=6), or

singleton WES (n=20)

43% 8 (12% of those with a diagnosis, 5% of those

tested) had directly altered clinical care

18 (11% of those tested, 26%

of those diagnosed)

Bourchany (2017)42;

France

High 29 unrelated adults and children

congenital anomalies and undiagnosed

developmental disorder seen at a

genetics center

WES 45% 2 (15.4%) of those with WES diagnosis had

change in prognosis.

6 (46.2%) of those with WES diagnosis had

change in inheritance pattern of presumed

diagnosis

1 (7.7%) of those with WES diagnosis had

investigation of systemic involvement

12/13 (92.3%) of those

diagnosed using WES received

prenatal counseling/testing

Cordoba (2018)18;

Argentina

Some 40 adults and children with suspected

neurogenetic conditions from a single

tertiary genetics clinic

Singleton or trio WES 40% 7 (43.8% of those with a diagnosis,

17.5% of those tested)

NR

Evers (2017)52;

Germany

Some 72 children from 60 families with

undiagnosed, suspected genetic

conditions and diverse phenotypes

Mostly trio WES with a

few cases including

affected or unaffected

siblings

35% 8 (38%) had management changes;

20 (95%) said results were

important for family planning

4 (19%) used results for

prenatal diagnosis

Iglesias (2014)46;

U.S.

Some 115 children and adults with diverse

phenotypes evaluated at an academic

health care center

Mostly trio WES 32% 8 (22%) screened for other manifestations of the

disease

14 (38%) had changes in management

5 (14%) identified other family

mutation carriers

6 (16%) had reproductive

planning

Matias (2019)49; U.S. Some 78 children with diverse phenotypes

from a tertiary children's hospital

Mostly trio WES 48% Change from pre-WES to post-WES

37 (100% of those with a diagnosis)

36 (97% of those with a

diagnosis


Final


Table 5. Summary of characteristics and outcomes for studies evaluating clinical utility among studies enrolling diverse populations (continued)

Author (Year);

Country

Risk of


Testing Strategies

Used

Diagnostic


Results Leading to

Additional Genetic


Family

Meng (2017)40; U.S. Some 278 infants less than 100 days old with

diverse clinical phenotypes referred to a

tertiary institution for WES

Majority singleton WES,

some trio WES

38% 53 (52% of those with diagnosis) 90 (88% of those with

diagnosis)

Niguidula (2018)39;

U.S.

High Survey of 62 health care providers

receiving patient WES report from

commercial laboratory for adult and

pediatric patients with diverse

phenotypes (2.2% of surveys returned)

WES not otherwise

described

37% Medication change: 11% of those tested, 17% of

those with diagnosis

Discontinued diagnostic studies: 58% of those

tested, 96% of those with diagnosis

Management change: 40% of those tested, 78%

of those with diagnosis

45% of those tested, 87% of

those with diagnosis

Nolan (2016)24; U.S. Some 50 children from a single academic

neurology clinic who were referred for

diagnostic WES testing

Singleton or trio WES NR 10 (19% of those tested, 42% of those with a

diagnosis)

11 (22% of those tested, 46%

of those with a diagnosis)

Sawyer (2016)55;

Canada

High 105 families of patients with diverse

phenotypes who had already received

standard of care genetic evaluation and

diagnostic testing

Singleton WES NR 6 (26%) of 105 families 3 had adjustment of therapy and 3 had therapy

initiated

NR

Srivastava (2014)57;

U.S.

High 71 children and adults with

neurodevelopmental disabilities and

negative diagnostic workup prior to WES

WES 41% 32 (41% of those tested, 100% of those

diagnosed)

27 (35%)

Stark (2016, 2017,

2019)13-16; Australia

Some 80 children age 0 to 2 years with diverse

phenotypes suspected of having

monogenic disorders and negative CMA

result

Singleton WES in

parallel with standard

non-WES tests

Standard tests:

28%

WES: 58%

16 (20% of those tested, 34% of those

diagnosed)

14 (30%)

Stark (2018)22;

Australia

High 40 acutely ill children and infants with

suspected monogenic disorder,

compared to 40 children from other

published articles

Singleton WES Rapid WES: 53%

Standard WES:

58%

Reported for the Rapid WES Cohort Only

16 (20% of those tested, 34% of those

diagnosed)

20% (16) of those tested

NR


Final



Author (Year);

Country

Risk of


Testing Strategies

Used

Diagnostic


Results Leading to

Additional Genetic


Family

Tan (2017)19;

Australia

Some 44 children age 2 to 18 years with

diverse phenotypes suspected of having

a monogenic condition from a single

tertiary pediatric hospital. All had prior

nondiagnostic CMA

Singleton WES with

targeted analysis of only

genes known to cause

monogenic disorders,

evaluated

counterfactual

strategies including

WES at first

presentation, at first

genetic appointment, as

final test, and without

WES

52% 7 (30% of those diagnosed, 16% of those tested)

had change in management (specific changes

unspecified).

6 (26% of those diagnosed, 14% of those tested)

had

1 (4% of those diagnosed, 2% of those tested) of

those stopped planned investigations.

1 (4% of those diagnosed, 2%

of those tested) had a prenatal

implantation genetic diagnosis

planned

Valencia (2015)54;

U.S.

Some 40 pediatric patients with diverse clinical

features referred by medical specialists

for exome sequencing.

Singleton WES 30% 13% (5 of 40) had a potential change in

management

2 (5%) had change in management

12 (30%) altered medical management including

genetic counseling

NR

Waldrop (2019)47;

U.S.

Some 31 pediatric patients belonging to 30

families seen in a neuromuscular clinic

who had WES performed since 2013

Trio WES 37% 3 (25%) plan for disease surveillance

1 (8%) discontinued medication

1 (8%) with certainty of malignant hyperthermia

risk

1 (8%) with diagnosis started palliative care

12 (100%)

Willing (2015)56; U.S. High 35 children with diverse phenotypes at a

children's hospital with an acute illness

of suspected genetic cause

Rapid WGS of trios with

whole exome analysis;

Standard genetic testing

based on clinical

judgment

Rapid WES 57%,

standard genetic

testing: 9%

12 (60%) had a change in management NR


Final



Author (Year);

Country

Risk of


Testing Strategies

Used

Diagnostic


Results Leading to

Additional Genetic


Family

Zhu (2015)45; U.S. High 113 trios reported for the first time and 6

previously unsolved trios from diverse

clinical phenotypes of children and

adults

Trio WES 24% 4 (3% of those tested, 14% of those diagnosed) NR

Abbreviations: CMA = chromosomal microarray analysis; NR = not reported; WES = whole exome sequencing; WGS = whole genome sequencing


Final


Two studies directly compared management changes in those who received a diagnosis to those

who did not. Matias et al. reported that 100% of probands with a diagnosis from WES

experienced some change in management, compared with 95% of those without.49 In the study

by Niguidula et al., 17% of those who received a molecular genetic diagnosis from WES

experienced a medication change, compared with 29% of those who received uncertain results

and 3% of those with negative results from WES.39

Twelve studies13-16,19,39,42,43,46,47,49,52 reported on additional genetic counseling or testing in family

members of sequenced probands. Three of these studies reported on cascade genetic testing in

family members due to WES, ranging from 14% to 97% of those with a diagnosis.16,46,49 Matias

et al. additionally reported that 0% of families who did not receive a molecular genetic diagnosis

from WES went on to receive cascade genetic testing.49 For the 8 studies that reported on

reproductive counseling, testing, planning, or prenatal diagnosis, between 4% and 97% of

families who received a diagnosis using WES used that information for the aforementioned

purposes.13-16,19,39,42,43,46,49,52 At the low end of this range, 2 studies both reported that 4% of

families receiving a diagnosis from WES either planned to use or reported using that information

for prenatal implantation genetic diagnosis.19,43 At the high end of this range, Matias et al.49

reported that 97% of families who received a diagnosis also received reproductive counseling,

while Bourchany et al.42 reported 92% of those who received a diagnosis using WES received

either prenatal counseling or testing. Three of these studies also reported on the percentage of

families who did not receive a diagnosis from WES, and found that between 0% and 6% sought

out reproductive planning services after receiving their negative WES results.13,39,49 In Waldrop

et al., all 12 individuals who received a molecular genetic diagnosis from WES received genetic

counseling regarding implications in family members.47

3.3.3 Findings From Studies Among Patients With Epilepsy

Characteristics and outcomes from the 5 studies that enrolled patients with epilepsy are reported

in Table 6. We rated all but 1 as having a high risk of bias.58 All 5 studies reported on actual

changes in management experienced by their patient population.27,28,32,34,58 The percent with an

actual change in management ranged from 0% to 31% of those who received a diagnosis from

WES. Between 0% and 20% of those diagnosed by WES underwent a change in medication as a

result of the WES results including one patient in a study for which the percentage was not

calculable.28 Palmer et al. additionally reported that of 16 patients, 1 (6%) patient diagnosed by

WES initiated palliative care, 1 (6%) patient had reduced number and expense of diagnostic

interventions, and 1 (6%) patient had additional unspecified management changes.27 For

comparison, Ream et al. reported that of 23 patients diagnosed by other genetic testing strategies,

and 3 (13%) changed medications and 1 (4%) was prescribed a special diet as a result of the

molecular genetic diagnosis.58


Final


Table 6. Summary of characteristics and outcomes for studies evaluating clinical utility among studies enrolling patients with epilepsy

Author (Year); Country

Risk of Bias

Study Population and Setting

Testing Strategies Used Diagnostic Yield Changes in Management

Results Leading to Additional Genetic Counseling or Testing in Family

Howell (2018)28;

Australia

High 49 infants with severe

epilepsy

7 strategies evaluated that included

different combinations of 3 tiers of

testing (Tier 1: imaging, CMA,

metabolic; Tier 2: mitochondrial

mutations, advanced metabolic

testing, CSF testing; Tier 3: skin

biopsy, electron microscopy,

histochemistry) with/without genetic

testing that included singleton WES,

but also included gene panel testing

Across the 7

strategies, range of

45% to 56%

Genetic diagnosis led to a management

change in 1 participant (SCN2A mutation

with sodium channel blocking AEDs

used); unclear what % this represents

since not calculable based on data

reported in article.

100% of those with a genetic

diagnosis; a significant

recurrent risk was identified in

5 families.

Palmer (2018)27;

Australia

High 30 children with

infantile-onset epileptic

encephalopathy who

remained undiagnosed

after “first-tier” testing at

a single children’s

hospital

Pediatric neurology and clinical

genetics consultation, first and second

tier testing (blood, urine, and CSF

chemistries and metabolic testing,

imaging, single gene, gene panel,

CMA, mitochondrial testing), trio WES

Without WES: 6%;

With WES: 53%

5 (31.3%) of those diagnosed 44% (7) of those with

diagnosed

Perucca (2017)34;

Australia

High 40 adults and children

with a diagnosis of focal

epilepsy

Singleton WES of 27 focal and 35

non-focal epilepsy genes, then

expanded to 29 focal epilepsy genes.

13% 1 (20%) of those with diagnosis had a

change in medication

NR

Ream (2014)58; U.S. Some 25 patients at tertiary

care center diagnosed

with pediatric drug

resistance epilepsy

Patients underwent one of the

following genetic tests - karyotype,

chromosomal microarray, gene

sequencing of specific single genes,

gene sequencing using gene

sequencing panels, and/or WES

WES: 17%; gene

panel 46%; single

gene 15%; microarray

17%; karyotype 14%;

any genetic testing

35%

0 (0%) of patients diagnosed by WES

3 (13%) patients diagnosed by other

genetic tests had a change in medication.

1 (4%) patients diagnosed by other

genetic tests was prescribed a special diet

3 (50%)


Final


Table 6. Summary of characteristics and outcomes for studies evaluating clinical utility among studies enrolling patients with epilepsy (continued)

Author (Year);

Country

Risk of

Bias

Study Population and

Setting Testing Strategies Used Diagnostic Yield Changes in Management

Results Leading to

Additional Genetic


Family

Snoeijen-

Schouwenaars

(2019)32; The

Netherlands

High 100 adults and children

with epilepsy from

outpatient specialty

clinics

Targeted epilepsy/ID panel followed by

WES if negative in mostly trios but

some singletons

25% 1 (4%*) of those with diagnosis NR

Abbreviations: CMA = chromosomal microarray analysis; CSF = cerebrospinal fluid; ID = intellectual and development disability; WES =whole exome sequencing


Final


Of these 5 studies, 3 reported that families of probands had undergone either additional genetic

counseling or reproductive planning as a result of their WES findings.27,28,58 Howell et al.

identified 5 families with a significant risk of disease recurrence who received genetic

counseling, though the number of eligible families was unclear from the study.28 Although no

patients received a molecular genetic diagnosis using WES in the study by Ream et al., the

authors report that 50% of the families whose probands underwent WES received genetic

counseling about heterozygous autosomal recessive mutations that the authors described as

having “potential diagnostic significance.”58 Finally, Palmer et al. reported that 44% of the

families whose probands were diagnosed using WES utilized reproductive planning services.27

Of the 5 studies, 2 reported on potential changes in management resulting from WES.32,58 Ream

et al. defined an a priori series of molecular genetic diagnoses that the authors felt would result

in potential management changes and reported that none of the 6 patients who had received a

diagnosis from WES would fall into that category, compared to 4 of 23 (17%) patients who had

received a diagnosis from a non-WES genetic test.58 Snoeijen-Schouwenaars et al.32 reported that

10 of 25 patients (40%) with a pathogenic or likely pathogenic WES result had a potential

change in management available to them.

3.3.4 Findings From Studies Among Patients With Other Single Phenotypes

Characteristics and outcomes from 7 studies that reported on the clinical utility of WES among

patients with a specific phenotype other than epilepsy are reported in Table 7.25,41,44,48,50,51,53

Reported changes in clinical management of the primary phenotype included dietary changes,

alterations to prescribed medications, or discontinuation of unnecessary treatment. Alternations

to medication or diet occurred in 4 of 33 (12%) patients presenting with short stature,51 10 of 45

(22%) patients presenting with neurodevelopmental disorders,25 9 of 23 patients (39%)

diagnosed with familial hypercholesterolemia,50 and 15 of 28 (54%) patients presenting with

neurometabolic disorders.44 In one study,25 unnecessary treatment was stopped for 3 of 45 (7%)

patients diagnosed. In the study of infantile malignant osteopetrosis, 1 of 6 patients was

redirected to palliative care because their specific genotype was untreatable.53 Only Jones et al.

reported changes in genetic counseling as a result of WES findings.50 They reported that 8 of 19

(42%) individuals discussed their WES results with a clinical genomics specialist. Mann et al.

reported that among 108 patients who had received a kidney transplant, their post-transplant

WES results indicated that better pre-transplant management options were available for 5 of

them.48


Final


Table 7. Summary of characteristics and outcomes for studies evaluating clinical utility among studies enrolling other single phenotype populations

Author (Year);

Country

Risk of

Bias Study Population and Setting Testing Strategies Used

Diagnostic

Yield Actual Changes in Management

Results Leading to

Additional Genetic


Family

Daga (2018)41;

U.S.

Some 65 children and adults from 15 families with

nephrolithiasis or nephrocalcinosis before

age 25

WES of multiple family

members or trios

29% 3 (20%) of diagnosed families NR

Hauer (2017)51;

Germany

High 200 adults and children with short stature

and extensive prior diagnostic workup

referred by their local medical specialists for

evaluation of growth retardation/ short stature

Targeted WES of known

short stature genes;

singleton WES in 100,

trio WES in 100.

17% 31 families (16%) led to preventive measures

23 families (12%) had orthopedic support and

developmental evaluation

9 families (5%) had recommendations for symptomatic

treatment or screening for associated malformations

4 families (2%) received new medications

NR

Jones (2018)50;

U.S.

High 28 individuals with WES results from the

MyCode initiative indicating a diagnosis of

familial hypercholesterolemia

Singleton WES NA 18 (78%) prescribed lipid-lowering therapy

8 (47%) changes to intensity of medication

management

9 (39%) changes made to their treatment regimens

1 (11%) was initiated on new therapy

8 (42%)

Mann (2019)48;

U.S.

Some 104 patients with chronic kidney disease who

developed disease before 25 years of age

and were transplanted

Targeted WES 32.70% 5 (4.8%) where diagnosis had clinical consequences

NR

Shamriz (2016)53;

Israel

High 6 patients clinically diagnosed with malignant

infantile osteopetrosis

Singleton WES 100% 1 (17%) decision to defer allogenic hematopoietic stem

cell transplantation based on clinical and genetic

findings

NR

Soden (2014)25;

U.S.

High 119 children with diverse

neurodevelopmental disorders

Trio WES 38.8% 49% (22 families with a diagnosis) NR

Tarailo-Graovac

(2016)44; Canada

Some 41 patients with intellectual developmental

disorder and unexplained metabolic

phenotypes

Trio WES with available

affected siblings

68% 18 (44% of those tested, 64%* of those diagnosed) NR

Abbreviations: WES =whole exome sequencing


Final


3.4 Key Question 2: Effectiveness (Health Outcomes)

Seven studies reported on health outcomes.22,32,34,40,53,56,58 Detailed information regarding study

characteristics and outcomes are reported in Appendix C, Table C-1 and Table C-3. The key

findings are:

Mortality ranged from 17% to 57%, but the studies that reported mortality were

conducted among infants in NICUs or hospitalized children with acute illness.

Among patients with epilepsy, management changes resulting from WES diagnosis

improved seizure control or behavior management in 0% to 3% of study participants.

We were unable to assess the certainty of evidence related to health outcomes because of very

serious limitations in the study designs and reporting of outcomes. Further details are in

Appendix G.


The included studies were published from 2014 to 2019 and had performed WES between 2011

and 2017. Two studies did not report when WES testing had taken place.53,58 One study was a

case series,53 one study was a controlled observational cohort,22 and the rest were single-arm

observational cohort studies. Three studies were conducted in the U.S.,40,56,58 2 were conducted

in Australia,22,34 and 1 each were conducted in Israel53 and The Netherlands.32 Two studies had

some industry funding,22,50,53 and 3 studies had no industry funding.34,40,56 Two studies did not

specifically list any study funders.32,58 Table 8 describes the characteristics of included studies

that reported health outcomes.

The number of probands who underwent WES testing in each study ranged from 6 to 278 (40%

to 52%; female). The median age of probands ranged from 26 days to 32.5 years. Three studies

only included probands under the age of 18,22,53,58 and 2 studies only included infants.40,56 Two

studies included both adults and children.32,34

Three of the included studies performed WES on probands with diverse phenotypes,22,40,56 and 3

studies included probands with epilepsy.32,34,58 The remaining study included only probands with

a clinical diagnosis of malignant infantile osteopetrosis.53 Two of the included studies performed

singleton WES,34,53 and 3 reported using a combination of singleton, duo, or trio WES.32,40,56

Four studies were conducted at institutions that were described as either tertiary22,34,58 or

quaternary,56 and the remaining 3 were described as academic medical institutions.32,40,53

3.4.2 Findings

Four publications reported mortality22,40,53,56 and none reported length of survival. Four studies

reported some other health outcome.32,34,53,58 Table 8 describes the characteristics and findings

for studies that reported health outcomes.


Final


Table 8. Summary of characteristics and findings for studies evaluating health outcomes

Author (Year);

Country

Risk

of

Bias

Study Population and Setting Testing Strategies Used Mortality

Other health outcomes

(morbidity, cognitive ability,

functional outcomes)

Meng (2017)40;

U.S.

Some 278 infants less than 100 days old with

diverse clinical phenotypes referred to a

tertiary institution for WES

Majority singleton WES, some

trio WES

5-yr death rate:

Diagnosed, 39 of 102 (38%)

Not diagnosed, 41 of 170 (24%)

120-day death rate:

Diagnosed: 30 of 102 (29%)

Not diagnosed: 28 of 170 (17%)

NR

Perucca (2017)34;

Australia

High 40 adults and children with a diagnosis of

focal epilepsy

Singleton WES of 27 focal and

35 non-focal epilepsy genes,

then expanded to 29 focal

epilepsy genes.

NR 1 experienced change from

"uncontrolled monthly seizures" to

seizure-free for 12 months since

implementing change in

management

Ream (2014)58;

U.S.

High 25 patients at tertiary care center diagnosed

with pediatric drug resistance epilepsy; 6 with

WES

Patients underwent one of the

following genetic tests:

karyotype, chromosomal

microarray, gene sequencing of

specific single genes, gene

sequencing using gene

sequencing panels, and/or WES

NR 0 of WES patients had improved

seizure control.

1 patient diagnosed with other

genetic tests had improved seizure

control based on medication

change resulting from gene test

Shamriz (2016)53;

Israel

High 6 patients clinically diagnosed with malignant

infantile osteopetrosis

Singleton WES 1 (17%) with diagnosis from WES

died 2 years after parent refusal of

treatment,

1 with diagnosis experienced

progressive neurological

deterioration.

4 with diagnosis were alive and

well

Snoeijen-

Schouwenaars

(2019)32; The

Netherlands

High 100 adults and children with epilepsy

previously received negative targeted gene

testing and had a clinical indication for WES

referred to outpatient specialty clinics

Targeted epilepsy/ID panel

followed by whole exome

analysis if negative in 66/100

trios and 34/100 singletons.

NR 1 patient had improved behavior

and mood following medication

change based on WES result

Stark (2018)22;

Australia

Some 40 acutely ill children and infants with

suspected monogenic disorder, compared to

40 children from other published articles

Singleton WES Unclear length of follow-up:

9 (23%) of rapid WES cohort

9 (11%) of standard WES cohort

NR


Final


Table 8. Summary of characteristics and findings for studies evaluating health outcomes (continued)

Author (Year);

Country

Risk

of

Bias

Study Population and Setting Testing Strategies Used Mortality

Other health outcomes

(morbidity, cognitive ability,

functional outcomes)

Willing (2015)56;

U.S.

High 35 children with diverse phenotypes

nominated for STATseq by treating physician

at quaternary children's hospital with an acute

illness of suspected genetic cause but without

a genetic diagnosis

Rapid WGS of trios with whole

exome analysis.

Comparator: standard genetic

testing based on clinical

judgment

120-day mortality:

14 (40%) overall

12 (57%) with a diagnosis

NR

Abbreviations: ID = intellectual and developmental disability; WES =whole exome sequencing; WGS = whole genome sequencing


Final


Health Outcomes Among Studies That Enrolled Diverse Phenotypes

Three studies reported outcomes among diverse phenotypes.13-16,22,40,56 Two of these studies

recruited critically ill children.22,56 One recruited children aged 0 to 2 years with suspected

monogenic disorders22 and one recruited infants through 100 days old who had been referred for

WES for a variety of medical concerns.56

Four studies reported mortality outcomes.22,40,53,56 Stark et al. reported that 9 of 40 (23%) infants

who underwent rapid WES died, compared to 9 of 80 (11%) infants who underwent standard

WES, though neither the length of follow-up time nor the percentage of those who died had

received a molecular genetic diagnosis were reported.22 Meng et al. reported 120-day mortality

for 30 of 102 (29%) participants with a diagnosis from WES, compared to 28 of 170 (17%)

participants who remained undiagnosed.40 At 5 years the mortality was 38% and 24%

respectively. Willing et al. reported a 120-day mortality in 12 of 21 (57%) critically ill infants

with a diagnosis from rapid or standard testing.56 Although they did not calculate a mortality rate

exclusive to infants who received a diagnosis from WES, 2 of 14 (14%) infants who did not

receive a genetic diagnosis by any method tested in this study died by 120 days.56 The studies did

not control for baseline differences, and in the three studies15,22,40,56 of rapid WES, sicker infants

were more likely to receive rapid WES.

Health Outcomes From Studies That Enrolled Participants With Epilepsy

Three studies included populations with epilepsy.32,34,58 None reported mortality information; all

reported on other health outcomes following medication changes due to WES results. Snoeijen-

Schouwenaars et al. reported 1 of 25 (4%) patients who received a diagnosis from WES had

improved behavior and mood symptoms after a medication changed that was based on the WES

results.32 Perucca et al. reported 1 of 5 (20%) patients who received a diagnosis from WES and a

corresponding change in epilepsy management improved from monthly seizures to having 12

months free from seizures.34 Ream et al. reported that 0 of 6 (0%) probands diagnosed using

WES had improved seizure control, compared with 1 of 23 (4%) probands who experienced a

medication change based on the results of other genetic tests.58

Health Outcomes From Studies That Enrolled Other Single-Phenotype Participants

One study reported health outcomes for 6 probands diagnosed with malignant infantile

osteopetrosis.53 In this study, 1 of 6 (17%) probands who had received a molecular genetic

diagnosis from WES died 2 years after receiving WES results and after parental refusal of further

treatment. There was no minimum follow-up required by this study; length of follow-up ranged

from 0.28 to 8.96 years. Of the other health outcomes reported, 4 of 6 (67%) probands were

described as “alive and well” and 1 of 6 (17%) had experienced progressive neurological

deterioration.


Final


3.5 Key Question 3: Safety and Harms

Twenty-six studies provided evidence on the harms associated with WES.20,24,40,42,43,50,54,58-76

Detailed information regarding study characteristics and outcomes are reported in Appendix C,

Table C-1 and C-4. The key findings are:

Two percent of patients diagnosed using standard testing were not diagnosed by

WES. The patients not diagnosed with WES had genetic variants that were not

diagnosed well by WES technology at the time the study was done.

We calculated the pooled percent of patients with an ACMG-defined medically

actionable variant to be 3.9% (95% CI, 2.4% to 5.3%) across 13 studies that provided

data suitable for use in pooling. Of the remaining studies, 4 reported 0% with

actionable variants,20,42,62,70 and the other 5 reported between 1% and 10%.24,54,61,68,75

Most patients or parents of patients did not experience psychosocial harms from

receiving negative or uncertain WES results; these findings come primarily from

qualitative research studies.

We assessed the certainty of evidence related to the frequency of ACMG-defined medically

actionable variants as low because of study designs and study limitations. We did not assess the

certainty of other reported safety outcomes as they were too heterogenous or largely reported

from qualitative research studies. Detailed certainty ratings are in Appendix G.


Twenty-six studies20,24,40,42,43,50,54,58-76 published between 2014 and 2019 provided evidence on

the harms associated with WES. The studies were conducted between 1998 and 2017. Twenty

studies20,24,40,42,43,54,58,60,61,63-66,69-74,76 were single-arm observational cohort studies. Twelve had a

low risk of bias,42,54,60,63,64,66,69-74 7 had some risk of bias,20,24,40,43,61,65,75 and 2 had a high risk of

bias.58,76 The single-modeling study75 was rated as having some risk of bias. We did not assess

the risk of bias for the 5 qualitative research studies.50,59,62,67,68 Sixteen studies received no

industry funding,20,24,40,42,43,54,59,60, 63,65,66,68-70,73,76 one study received some industry funding72 and

4 studies were completely funded by industry.50,61,64,71 Funding source was not reported for the

remaining 5 studies.58,62,67,74,75

Two studies71,75 were conducted in Australia and one each in Canada,72 France,42 Japan,70 The

Netherlands,20 and Saudi Arabia.61 The remainder of the studies were conducted in the U.S. One

study40 limited enrollment to infants, 6 to children,20,24,54,58,62,72 and 4 to adults.50,63,67,76 Three

studies65,70,75 did not report the age range of their participants, and the remainder included both

children and adults. Twenty studies20,24,40,42,43,54,59-69,74-76 included patients with diverse

phenotypes, and 6 studies included patients with a single phenotype.50,58,70-73

3.5.2 Findings

The harms potentially resulting from WES include false positive or false negative test results and

downstream adverse health outcomes or distress, distress resulting from receipt of undesired

information on other genetic conditions or family relationships, and other sources of


Final


psychosocial harm. We found evidence regarding each of these potential harms. None of the

studies reported other types of harms.

Misdiagnosis

We report the frequency of sequencing errors and overdiagnosis in Section 3.2.3, the contextual

questions on diagnostic yield. One study20 compared the proportion of patients with missed

diagnoses with WES and with standard testing (Table 9). The authors found that 3 of 150 (2%)

patients diagnosed by standard testing were not diagnosed by WES. These 3 patients had genetic

diseases caused by copy number variants or trinucleotide repeat expansions, variants that are less

likely to be detected by WES.

Table 9. Study and population characteristics of the one controlled cohort studies evaluating missed diagnoses in WES compared to standard genetic testing


Study Design; Risk of Bias

Population Characteristics Intervention (N Missed; N Analyzed)

Comparator (N Missed; N Analyzed)

Vissers (2017), Netherlands

Single-arm observational cohort, Some

Children with nonacute neurological symptoms of suspected but undiagnosed genetic origin.

WES: 3; 150 Standard testing pathway: 36; 150

Abbreviations: WES = whole exome sequencing

Reported Secondary Findings (ACMG-Defined Medically Actionable Variants)

Twenty-two studies reported on the proportion of participants who received results on genetic

variants that had health implications for diseases other than the one for which they were

evaluated (Table 10).20,24,40,42,43,54,58,60-66,68,70-76 We rated 11 of the studies as having a low risk of

bias,42,54,60,63,64,66,70-74 7 as having some risk of bias,20,24,40,43,61,65,75 and 2 as having a high risk of

bias.58,76 The remaining 2 studies had qualitative study designs that were nested within larger

studies.62,68 Four studies reported on the proportion of patients who chose to receive information

on secondary findings.43,60,64,74 Three60,64,74 of these studies had a low risk of bias, and 1 had

some risk of bias.43

Among the studies that reported on the proportion of patients who chose to receive secondary

findings, 90% (2,781 of 3,089 patients) opted to receive such findings. In a survey of participants

who chose not to receive nonmedically actionable results, 5 of 36 respondents said they feared

the information would be an emotional burden.76

We calculated that the pooled percent of patients with an ACMG-defined medically actionable

variant to be 3.9% (95% CI, 2.4% to 5.3%) across 13 studies (6,653 participants) that provided

data suitable for use in pooling. Of the remaining studies, 4 reported 0% with actionable

variants,20,42,62,70 and the other 5 reported between 1% and 10%.24,54,61,68,75


Final


Table 10. Study and population characteristics of the 22 studies that reported on the frequency of ACMG-defined medically actionable variants and participants’ choice to receive them



Population Characteristics

Patients With ACMG-Defined Medically Actionable Variants

Proportion of Patients Who Chose to Receive Reports of ACMG-Defined Medically Actionable Variants

N, With Variants

N, Analyzed for ACMG Variants

N Total With WES

Baldridge (2017); U.S.

Single-arm observational; Some

Patients with diverse phenotypes who were referred by exome clinic; mixed children and adults

14 141 141 146

Bourchany (2017); France

Single-arm observational; Low

Patients who had been seen at genetics centers for congenital anomalies or undiagnosed DD; Mixed children and adults

0 29 NR 29

Ding (2014); Australia

Modeling Study; Some

24 autosomal-dominant, highly penetrant conditions characterized by long asymptomatic periods and response to preventive measures or treatment. Modeling only, no participants.

NR (2% to 7%)

NA NA NA

Jurgens (2015); U.S.


Families with diverse apparent Mendelian conditions that had undergone WES at academic medical center. Characteristics of sequenced individuals not described.

2 232 NR 232

Lee (2015); U.S.


Children who had been seen at ophthalmic genetics clinic for diverse ophthalmic conditions.

1 26 NR 26

Meng (2017); U.S.


Infants < 100 days old with diverse phenotypes who had been referred for exome sequencing

21 267 NR 267

Monies (2017); Saudi Arabia


Families (affected children and parents) that had been referred for multigene panels or WES; diverse phenotypes

NR (1%) NR NR NR

Muramatsu (2017); Japan


Patients with inherited bone marrow failure syndromes. Patient characteristics not described.

0 250 NR 250

Nolan (2016); U.S.


Pediatric neurology patients with diverse neurologic phenotypes

NR (10%)

NR NR NR

Posey (2015); U.S.


Unrelated adults with diverse phenotypes who had received clinical WES

6 482 NR 482

Ream (2014); U.S.

Single-arm observational; High

Children with drug-resistant epilepsy 4 6 NR 6

Retterer (2016); U.S.


Children who had been tested at commercial laboratory with diverse phenotypes

129 2091 2091 2382

Roche (2019); U.S.


Adults who had received WES and education on nonmedically actionable secondary findings

13 622 NR 622


Final


Table 10. Study and population characteristics of the 22 studies that reported on the frequency of ACMG-defined medically actionable variants and participants’ choice to receive them (continued)



Population Characteristics

Patients With ACMG Medically Actionable Variants

Proportion of Patients Choosing to Receive Reports of ACMG Medically Actionable Variants

N, With Variants

N, Analyzed for ACMG Variants

N Total With WES

Rosell (2016); U.S.

Qualitative Parents whose children had undergone WES 0 19 NR 19

Shashi (2016); U.S.


Children and adults who received clinical WES for diverse phenotypes

2 59 59 59

Strauss (2017); U.S.


Old Order Amish and Mennonite children with diverse phenotypes

21 490 490 502

Tammimies (2015); Canada


Children with autism who were referred by developmental pediatric clinics

6 95 NR 95

Valencia (2015); U.S.


Children who had received physician-ordered WES with diverse phenotypes

NR (8%) NR NR NR

Vanderver (2016); Australia


Children and adults with suspected diagnosis of leukodystrophy or genetic leukoencephalopathy

3 142 NR 142

Vissers (2017); Netherlands


Children with nonacute neurological symptoms of suspected but undiagnosed genetic origin

0 150 NR 150

Werner-Lin (2018); U.S.

Qualitative research design

Adolescents and parents who were from disease-specific clinics with diverse phenotypes

NR (10%)

NR NR NR

Yang (2014); U.S.


Children and adults who had received physician-ordered WES with diverse phenotypes

59 2,000 NR 2000

Abbreviations: ACMG = American College of Medical Genetics and Genomics; NR = Not Reported; U.S. = United States;

WES = Whole Exome Sequencing

Psychosocial Harms

Eight studies (3 quantitative66,69,76 and 5 qualitative50,59,62,67,68) provided evidence on the

psychosocial harms or other reactions experienced by patients who underwent WES or their

parents (Table 11).

The studies provided little indication of significant psychosocial harms from receiving

nondiagnostic or uncertain WES results. Anxiety and depression was higher among parents of

undiagnosed children than normative populations, but did not differ between parents whose

children had a nondiagnostic WES and those whose child had not.69 The initial reaction of

parents and patients who received uncertain WES results included frustration, disappointment,

stress, anger and fear,62,67,68 but one study68 found these feeling had resolved within 3 months.

Parents reported that the uncertain result did not affect their ability to take care of their child or

alter their perception of their child's condition.67 Some families felt a need for more follow-up

counseling or outreach or a need to help families manage expectations.62


Final


A third study of reactions to variants of unknown significance (VUS) found that almost all

participants understood the VUS was not a definitive diagnosis.59 One participant in this study

experienced distress from the VUS result, but no participants regretted getting the result or the

test. Most felt the information would be useful in the future.

No studies directly examined the impact of WES results on family dynamics or relationships. In

one case, a participant experienced shock after discovering nonpaternity during a family

discussions of the WES result and the family's history of heart disease.50 One study reported on

the uptake of cascade testing among family members.66 Among the families of 92 patients with a

medically actionable secondary finding, 33 relatives from 19 families requested testing for the

variant found in the proband.

Table 11. Study and population characteristics of the 8 studies reporting on psychosocial harms and reactions among patients and parents of patients who underwent WES



Population Characteristics Sample Size

Study Findings

McConkie-Rosell (2018); U.S.


Parents of children with suspected genetic disorder

50 Instrument All Nondiagnostic WES

Norm

GAD-7 4.9 ± 4.38 5.36 ± 4.96 3.57 ± 3.38

PHQ-9 4.8 ± 4.76 5.45 ± 5.99 2.91 ± 3.52

CSE 186 ± 44 188 ± 38 137 ± 46

HCEI subscales

ICCE 18.0 ± 2.2 18.5 ± 1.8 15.9 ± 2.6

TU 16.3 ± 2.7 16.2 ± 2.7 17.4 ± 2.3

Yang (2014); U.S. Single-arm observational; Low

Patients with physician-ordered WES

2,000 Of 92 patients with a medically actionable secondary finding, 33 relatives from 19 families requested testing for the variant found in the proband

Jones (2018); U.S.

Qualitative; NA

Apparently healthy clinic patients who received WES for routine care and were found to carry familial hypercholesterolemia

23 Shock from discovery of nonpaternity discovered in family discussion of family history of heart disease

Li (2019); U.S. Qualitative; NA

Parents of children whose WES results included a VUS

14 Range of emotions from VUS result, including confusion, anger, stress, fear., relief, and disappointment. Majority of participants reported VUS result did not affect their ability to take care of their child or alter their perception of their child's condition.

Roche (2019); U.S.


Patients who received WES and education about nonmedically actionable secondary findings

155 5 of 36 respondents stated their reason for not requesting nonmedically actionable secondary findings were concern that the information would be an emotional burden.

Rosell (2016); U.S.

Qualitative; NA

Parents whose child had undergone WES

19 All parents hoped for diagnosis. 21% had high expectations of diagnosis; 68% had tempered expectations; 11% had low expectations. Some parents voiced frustration and disappointment with long waiting and not getting complete answers. Some families felt need for more follow-up counseling or outreach and some expressed need to help families manage expectations.


Final


Table 11. Study and population characteristics of the 8 studies reporting on psychosocial harms and reactions among patients and parents of patients who underwent WES (continued)



Population Characteristics Sample Size

Study Findings

Rosell (2016); U.S.

Qualitative; NA Parents whose child had undergone WES

19 All parents hoped for diagnosis. 21% had high expectations of diagnosis; 68% had tempered expectations; 11% had low expectations. Some parents voiced frustration and disappointment with long waiting and not getting complete answers. Some families felt need for more follow-up counseling or outreach and some expressed need to help families manage expectations.

Skinner (2018); U.S.

Qualitative; NA Patient with VUS 32 1 (3%) misinterpreted an uncertain result as a definitive answer. Some adult participants for whom family testing was recommended did not pursue it because they did not want to pressure family members. Patients pursuing testing did not worry while waiting on results. Some commented that uncertainty was not new. One participant reported experiencing distress related to the uncertain result. No participants expressed regret at learning the uncertain result. No participants reacted to the uncertain result in ways that could cause harm. Most regarded the information as potentially valuable in the future.

Werner-Lin (2018); U.S.

Qualitative; NA Adolescents and parents from disease-specific clinics

10 Initially disappointed with uncertain results; Experienced frustration, disappointment, and fear. Feelings evolved over time; and moved toward acceptance and satisfaction, generally within the ensuing 3 months.

Abbreviations: CSE = Coping Self-Efficacy Scale; GAD-7 = Generalized Anxiety Disorder; HCEI = Health Care Empowerment

Inventory; ICCE = Informed, Committed, Collaborative, Engaged score from HCEI; PHQ-9 = Patient Health Questionnaire; NA

= not applicable; TU = Tolerance of Uncertainty from HCEI; VUS = variants of uncertain significance; WES = whole exome

sequencing

3.6 Key Question 4: Costs

Seventeen studies (reported in 20 publications) reported cost-related outcomes.13-28,77-80 Detailed

information regarding study characteristics and outcomes are reported in Appendix C, Table C-1,

Table C-5, and Table C-6. The key findings are:

The cost of a WES test reported in studies varied between US$ 1,000 and US

$15,000; trio WES costs more than singleton WES.

In both single-phenotype and diverse phenotype populations, when compared to

standard diagnostic pathways, testing pathways that used WES identified more

diagnoses at a lower cost in some studies, or identified more diagnoses but at a

somewhat higher cost in other studies (range US$ 1,775 to US$ 8,559 higher

depending on where WES was used in the testing pathway).


Final


Pathways with earlier WES testing were more likely to be cost savings than pathways

that used WES later in the testing pathway or as a last resort strategy.

We assessed the certainty of evidence related to all cost outcomes as very low because of study

designs, study limitations, inconsistency, and imprecision. Detailed certainty ratings are in

Appendix G.


Study characteristics are briefly summarized in Table 12. Two of the 17 studies were conducted

in the U.S.;24,25 the rest were conducted in Australia (10), The Netherlands (3), Canada (2), and

Argentina (1). One study was conducted using data from the years 1998 to 2013,26 and most

were conducted from 2011 to 2017. Eight studies were funded in part by industry funding;13-

17,19,22,25,27,78,79 the rest were government agency funded (5) or the source of funding was not clear

(4). We assessed 6 studies as having a high risk of bias,17,18,24,25,77,78 and the rest we assessed as

having some risk for bias. Sources of bias were generally related to inadequate information about

costing methodology and model assumptions, lack of incremental analysis, and selective

outcome reporting.

Sample size ranged from 14 to 370 participants across these studies. Three studies were

conducted among populations that included both children and adults;18,21,78 the rest were

conducted exclusively among infants or children. Ten studies were conducted among populations

that included diverse phenotypes.13-25 The other 7 studies enrolled populations with homogenous

phenotypes including participants with autism,79 congenital muscular dystrophy,26 epilepsy,27,28

IDD,77,80 and peripheral neuropathy.78

The testing strategies evaluated across the studies were highly varied and no single testing

strategy was evaluated by more than one study. Only 3 studies were explicitly reported as being

conducted prospectively.13-16,19,78 Four studies used simulation or modeling to derive cost-related

outcomes.17,23,28,79 One study used a controlled cohort design26 and the remaining 13 studies used

a single-arm observational cohort design. The most commonly reported outcomes in this body of

evidence were cost per patient for the testing strategy involving WES. Eight studies also reported

cost per additional diagnosis because an actual or counterfactual comparison was available.13-

16,19,21,26-28,78,79 Only one study reported cost per quality life year (QALY) gained.13-16


Final


Table 12. Summary of characteristics for studies evaluating cost outcomes related to whole exome sequencing

Author (Year); Country; Perspective

Risk of Bias


Testing Strategies Used Diagnostic Yield

Costs Included Outcomes reported

Single-phenotype Populations

Howell (2018)28 Australia; Payer

Some 49 infants with severe epilepsy

7 strategies evaluated that included different combinations of 3 tiers of testing (Tier 1: imaging, CMA, metabolic; Tier 2: mitochondrial mutations, advanced metabolic testing, CSF testing; Tier 3: skin biopsy, electron microscopy, histochemistry, others) with/without genetic testing that included singleton WES with targeted analysis, but also included gene-panel testing

Across the 7 strategies, range of 45% to 56%

All costs related to diagnostic testing, including office visits, costs associated with sedation or operating rooms for diagnostic procedures

Cost of WES gene panel Cost per patient Cost per diagnosis Cost per additional diagnosis

Monroe (2016)80 Netherlands; Payer

Some 17 children with IDD from a tertiary specialty clinic

Trio WES; traditional diagnostic evaluation including lab testing, imaging, and genetic tests other than WES

Trio WES: 30% Costs of inpatient and outpatient medical interventions, imaging, and diagnostics, health professional visits. In the comparative analyses, WES costs replace all other genetic testing costs except CMA

Cost of WES Cost per patient

Palmer (2018)27 Australia; Payer

Some 30 children with infantile-onset epileptic encephalopathy who remained undiagnosed after “first-tier” testing at a single children’s hospital

Pediatric neurology and clinical genetics consultation, first and second tier testing (blood, urine, and CSF chemistries and metabolic testing, imaging, single gene, gene panel, CMA, mitochondrial testing), trio WES

Without WES: 6% With WES: 53%

Actual costs of diagnostic test Cost of WES? Cost per patient Cost per diagnosis Cost per additional diagnosis


Final


Table 12. Summary of characteristics for studies evaluating cost outcomes related to whole exome sequencing (continued)


Risk of Bias




Schofield (2017)26 Australia; Payer

Some 56 children seen at a single tertiary neuromuscular center for congenital muscular dystrophy or nemaline myopathy

Traditional diagnostic pathway (metabolic testing, nerve conduction testing, imagine, muscle biopsy, candidate gene testing, CMA); Traditional pathway plus neuromuscular gene panel (464 genes); traditional pathway followed by singleton WES then trio WES if remained undiagnosed

Traditional pathway: 46% Neuromuscular gene panel: 75% WES: 79%

Cost of all diagnostic investigations and procedures, including neuromuscular gene panel and WES

Cost of WES Cost per patient Cost per diagnosis Cost per additional diagnosis

Tsiplova (2017)79 Canada; Payer

Some Synthetic modeling population for children with autism using a microcosting approach from a single tertiary pediatric hospital

CMA alone; CMA plus singleton WES

NR Labor and material costs for CMA and WES; costs of confirmatory follow-up testing

Cost of WES Cost per patient Cost per additional diagnosis

Vrijenhoek (2018)77 Netherlands; Payer

High 370 children with intellectual disabilities who had diagnostic WES at a single tertiary pediatric hospital

Trio WES 35% Costs of all health care activities starting with the first visit to the medical center



Final




Risk of Bias




Walsh (2017)78 Australia; Payer

High 50 adults and children with neurophysiologically confirmed peripheral neuropathy suspected of having a monogenic cause

Singleton WES with initial analysis targeted to 55 genes, expanded to 88 genes plus SNP array if initial test nondiagnostic, then expanded to whole exome analysis if still nondiagnostic.

Initial WES 55 gene panel: 24% Expanded analysis and SNP array: additional 2 cases, cumulative yield 28% Whole exome analysis: additional 8 cases, cumulative yield 40%

Cost for all investigations, diagnostic procedures, first three neurology appointments for children, first neurology appointment for adults


Diverse Phenotype

Cordoba (2018)18 Argentina; Payer

High 40 adults and children with diverse phenotypes and suspected neurogenetic conditions seen at a single tertiary genetics clinic, mean age 23 years

Singleton or trio WES 40% Costs of tests, procedures, and visits

Cost of expendable diagnostic workup

Dillon (2018)17 Australia; Payer

High Simulation based study using data from 145 children with diverse phenotypes who had undergone diagnostic WES testing

WES with analysis limited to genes known to cause monogenic disorders; comparator strategies were simulated by applying up to 3 commercial gene panels to each child diagnosed with WES

54% Cost of WES, costs of comparison gene panels

Cost of WES Other cost-related outcome


Final




Risk of Bias




Dragojlovic (2018)23 Canada; Payer

Some Data from 167 families used in a model evaluating pediatric diagnostic exome sequencing, no further study population information provided

Singleton and trio WES with targeted analysis focused on genes known to cause disease; reanalysis of nondiagnostic results every 6 to 12 months

Singleton WES: 28%; trio WES (after genomics consultation): 34%; Trio WES without consultation: 34%

Labor costs of clinical and laboratory staff, WES infrastructure, laboratory, and bioinformatics costs

Cost of WES Cost per patient Cost per diagnosis

Ewans (2018)21 Australia; Payer

Some 14 adults and children with diverse phenotypes thought to have a monogenic etiology from a single clinical genetics unit

Singleton and trio WES, with whole exome analysis; reanalysis after 12 months for participants who were undiagnosed after initial testing

Initial WES: 30% (46% trio; 22% singleton) After reanalysis: 41%

Cost for diagnostic encounters and procedures


Nolan (2016)24 U.S.; Payer

High 50 children from a single academic neurology clinic who were referred for diagnostic WES testing

Singleton or trio WES with whole exome analysis

48% Costs for initial and secondary genetic and metabolic tests, including karyotype, CMA, methylation PCR, single-gene and gene-panel testing


Soden (2014)25 U.S.; Payer

High 119 children with diverse neurodevelopmental disorders

Trio WES 39% Costs of prior negative diagnostic testing for children who received a diagnosis, including lab, imaging, electromyograms, NCV studies. Not considered: physician visits, tests for patient management (e.g. EEG)

Cost of WES Cost per diagnosis


Final




Risk of Bias




Stark (2016, 2017, 2019)13-16 Australia; Payer

High 80 children age 0 to 2 years with diverse phenotypes suspected of having monogenic disorders and negative CMA result

Singleton WES in parallel with standard non-WES tests

Standard care: 28% WES: 58%

All investigations, procedures, and assessments that occurred for diagnostic purposes; for cost-effectiveness also considered costs of future care after diagnosis

Cost of WES Cost per patient Cost per diagnosis Cost per additional diagnosis Cost per QALY gained

Stark (2018)22 Australia; Payer

Some 40 acutely ill children and infants with suspected monogenic disorder, compared to 40 children from other published articles.13-16

Singleton WES, with likely whole exome analysis

Rapid WES: 53% Standard WES: 58%

Costs for all diagnostic investigations, procedures, and assessments

Cost of WES Cost per patient Cost per diagnosis

Tan (2017)19 Australia; Payer

Some 44 children age 2 to 18 years with diverse phenotypes suspected of having a monogenic condition from a single tertiary pediatric hospital. All had prior nondiagnostic CMA

Singleton WES with targeted analysis, evaluated counterfactual strategies including WES at first presentation, at first genetic appointment, as final test, and without WES

52% All diagnostic costs (inpatient and outpatient) from initial presentation to tertiary services for diagnostic assessment, including travel from home


Vissers (2017)20 Netherlands; Payer

Some 150 children through age 18 with nonacute neurological symptoms of suspected genetic origin from a single tertiary referral center

Singleton or trio WES, initial targeted analysis based on phenotype with expansion of analysis if no diagnosis; non-WES pathway was determined by providers and may have included CMA, single gene tests

Standard pathway: 7% WES: 29%

Actual costs of diagnostic tests both prior to and after inclusion in the study


Abbreviations: CMA = chromosomal microarray analysis; WES =whole exome sequencing


Final


3.6.2 Findings

Cost of WES Testing

The cost of WES testing varied by whether singleton or trio testing was used; the lowest reported

cost was US$ 1,000 and the highest reported cost was US $15,000. In general, trio WES cost

more than singleton WES.

Findings Among Diverse Phenotype Populations

Cost-related outcomes from the 10 studies enrolling diverse phenotype populations are reported

in Table 13. These studies evaluated various diagnostic pathways that included one or more of

the following strategies: a standard diagnostic pathway without WES testing, WES as a last

resort strategy, early WES (such as at initial tertiary presentation or initial clinical genetics

presentation), rapid WES, and WES reanalysis.

Five studies reported cost per patient comparing a standard diagnostic pathway to one or more

pathways that included WES testing.13-16,19-22 The cost per patient for the standard diagnostic

pathway ranged from AU$ 4,734 to €10,685. The cost per patient for diagnostic pathways that

included WES testing ranged from CA$ 5,263 to AU$ 8,384. Across the studies that evaluated

multiple WES testing strategies, pathways that involved earlier WES testing cost less than

pathways that used WES later in the pathway.13-16,19-21,23

The cost per diagnosis was reported in 4 studies and ranged from AU$ 10,843 to US$ 24,215.13-

16,19,21-23 Similarly, pathways that involved earlier WES testing cost less per diagnosis than

pathways that used WES later in the pathway. Furthermore, the 2 studies that involved reanalysis

of WES after an interval of 12 to 18 months cost less per diagnosis then pathways without

reanalysis.21 For example, Ewans et al. (conducted in Australia but reported in US$) reported a

cost per diagnosis of $23,010 (95% CI, $10,135 to $102,147) for WES at initial symptom

presentation, a cost per diagnosis of $24,215 (95% CI, $11,195 to $103,173) for WES at the time

of the clinical genetics review (a later stage in the diagnostic pathway), and a cost per diagnosis

of $15,653 (95 %CI, $7,619 to $49,752) for WES at initial presentation with reanalysis at 12

months. A similar pattern was observed in the Stark et al. study with respect to the cost per

diagnosis with reanalysis.13-16

Three studies reported the cost per additional diagnosis with WES testing when compared to a

standard diagnostic pathway.13-16,19,21 Test strategies that involved early WES testing generally

cost less and identified more diagnoses when compared to the standard pathway (range of

estimates US$ -586 to AU$ -6,482). The reported cost per additional diagnosis for WES when

used after some initial tertiary evaluations, but not as a last resort, ranged from US$-3,709 to

AU$ 2,622 when compared to the standard diagnostic pathway. Two studies reported the cost per

additional diagnosis for WES as a last resort strategy and estimates of the cost per additional

diagnosis were US$ 4,804 and AU$ 8,112.13-16,19 With one exception,19 estimates were imprecise

and confidence intervals did not exclude $0.


Final


Table 13. Summary of cost-related outcomes from studies enrolling diverse phenotype populations



Currency; Year Cost of WES

Cost Per Patient; Mean (95% CI)

Cost Per Diagnosis; Mean (95% CI)

Cost Per Additional Diagnosis; Mean (95% CI)

Other Cost Outcomes

Cordoba (2018)18 Argentina

40 adults and children suspected neurogenetic conditions

US$; NR Singleton or trio; $1,000

NR NR NR Cost of expendable diagnostic workup: $1,646 (95% CI, $1,439 to $1,835)

Dillon (2018)17 Australia

Simulation using data from 145 children who had undergone diagnostic WES testing

AU$; 2016 $2,000 NR NR NR In 26% of WES-diagnosed children for whom a comparator panel would have been diagnostic, the least costly panel had a higher price than the price of WES in this study

Dragojlovic (2018)23 Canada

Data from 167 families used in a model evaluating pediatric diagnostic exome sequencing

CA$;2016 Singleton; $2,576 Trio $6,437

Last resort singleton WES: $5,125 Last resort trio WES after consultation: $6,138 Last resort trio WES without consultation: $5,263

Singleton WES: $18,223 Trio WES after consultation: $14,405 Trio WES without consultation: $15,495

NR NR

Ewans (2018)21 Australia

14 adults and children monogenic etiology from a clinical genetics unit

US$; 2016 Singleton; $1,200 Trio; $3,150

Traditional path: $6,742 ($5,262 to $8,432) WES at initial presentation: $6,574 ($4,831 to $8,524) WES at clinical genetics review: $6,918 ($5,358 to $8,763) WES at initial presentation and reanalysis at 12 months: $6,709 ($4,937 to $8,688) WES at clinical genetics review and reanalysis at 12 months: $7,053 ($5,458 to $8,929)

Traditional path: $0 (no diagnoses made) WES at initial presentation: $23,010 ($10,135 to $102,147) WES at clinical genetics review: $24,215 ($11,195 to $103,173) WES at initial symptoms presentation and reanalysis at 12 months: $15,653 ($7,619 to $49,752) WES at clinical genetics review and reanalysis at 12 months: $16,457 ) $8,521 to $50,531)

Compared to traditional path: WES at initial symptoms presentation: $-586 (95% CI, $-3,769 to $16,144) WES at clinical genetics review: $618 (95% CI, $-2,431 to $17,439) WES at initial symptoms presentation and reanalysis at 12 months: $-77 (95%CI, $-2,990 to $7,334) WES at clinical genetics review and reanalysis at 12 months: $726 ($-1,873 to $8,060)

NR


Final


Table 13. Summary of cost-related outcomes from studies enrolling diverse phenotype populations (continued)







Other Cost Outcomes

Nolan (2016)24 U.S.

50 children from a neurology clinic referred for diagnostic WES testing

US$; NR Range $2,000 to $15,000

NR NR NR Average cost of initial and secondary genetic and metabolic testing prior to WES: $4,853 If WES was performed after initial but prior to secondary testing, estimated average savings of $2,968

Soden (2014)25 U.S.

119 children with neurodevelopmental disorders

NR; NR NR NR NR NR At an average cost of prior testing of $19,100 per family, WES would be cost-effective at $2,996 per individual

Stark (2016, 2017, 2019)13-16 Australia

80 children age 0 to 2 years suspected of having monogenic disorders

AU$;2015 Singleton; $1,500 to $3,100

Standard path: $ 4,734 ($3,693 to $ 5,895) WES after basic and complex investigations: $ 8,384 ($ 7,079 to $ 9,619) WES after basic investigations: $ 5,914 ($ 5,243 to $ 6,641) WES as first-tier test: $3,752 ($ 3,752 to $ 3,752) For those with noninformative initial testing: Reanalysis at 18 months: $ 391 ($ 360 to $ 433)

Standard path: $ 27,050 ($ 15,366 to $ 68,530) WES after basic and complex investigations: $13,415 ($ 10,165 to $ 18,351) WES after basic investigations: $ 9,462 ($ 7,497 to $ 12,619) WES as first-tier test: $ 6,003 ($4,841 to $ 7,899) For those with noninformative initial testing: Reanalysis at 18 months: $ 2,838 ($1,569 to $10,450)

Compared to standard path: WES after basic and complex investigations: $ 8,112 ($ 5,851 to $ 11,967) WES after basic investigations: $ 2,622 ($ 847 to $ 4,459) WES as first-tier test: $ -2,182 ($-5,855 to $ 130) In 97% of simulations, WES as first-tier test was dominant (less cost with more diagnoses compared to standard care).

Results from 2017 publication:13 Compared to standard path after median 473 days: Cost per QALY gained AU$ -1,578 (95% CI, AU$ -205,450 to AU$19,780). [resulting changes in management for proband only]. Cost per QALY gained AU$ 8,119 (95% CI, AU$ 1,962 to AU$ 38,944) [Changes in proband management, cascade


Final









Other Cost Outcomes

Stark (2016, 2017, 2019)13-16 Australia

Reanalysis every 6 months: $ 1,031 ($ 988 to $ 1,071) No reanalysis: $ 537 ($ 159 to $ 1,051)

Reanalysis every 6 months: $ 7,475 ($3,625 to $30,400) No reanalysis: NA (no diagnoses)

Compared to no reanalysis: Reanalysis at 18 months: $ -1,059 ($ -10,502 to $ 1,937) Reanalysis every 6 months:$3,578 ($ -232 to $17,003)

testing, and reproductive planning in first-degree relatives]. Results from 2019 publication projecting health outcomes over 20 years compared to standard care:15 WES after basic investigations: cost per QALY gained $ 31,144 (probands only); $ 20,840 (probands plus cascade outcomes in 1st degree relatives); $ 14,235 (probands, cascade outcomes in 1st degree relatives, reproductive outcomes)

Stark (2018)22 Australia

40 acutely ill children and infants with suspected monogenic disorder, compared to 40 children from other published articles.13-16

AU$; NR NR Usual care + conventional sequencing costs: $ 4,734 Standard WES: $ 6,777 Rapid WES: $ 7,029

Usual care + conventional sequencing: $27,050 ($15,366 to $68,530) Standard WES: $10,843 ($7,488 to $14,090) Rapid WES: $13,388 (95% CI, $9,269 to $17,507)

NR NR


Final









Other Cost Outcomes

Tan (2017)19 Australia

44 children age 2 to 18 years suspected of having a monogenic condition

US$; 2015 Singleton; <AU$2,300

Standard path: $7,515 ($5,743 to $9,486) Standard path with WES: $9,800 ($8,033 to $11,758) WES at first genetics appointment: $5,349 ($4,583 to $6,295) WES at first tertiary presentation: $3,927 ($3,520 to $4,413)

Standard path: NA (study design assumed no diagnoses made) Standard path with WES: $18,762 ($13,640 to $26,628) WES at first genetics appointment: $10,239 ($7,667 to $14,614) WES at first tertiary presentation: $7,534 ($5,832 to $10,494)

Compared to standard path: Standard path with WES: $4,804 ($3,904 to $6,523) WES at first genetics appointment: $-3,709 ($-7,491 to $ -694) WES at first tertiary presentation: $-6,412 ($-11,192 to $-2,887)

NR

Vissers (2017)20 Netherlands

150 children through age 18 with nonacute neurological symptoms of suspected genetic origin

€; 2016 €3,240 Standard path: €10,685 (€9,544 to €11,909) WES pathway: €9,941* WES-first pathway: €8,356 (€7,591 to €9,247)

NR NR NR

Abbreviations: AU$ = Australian Dollar; CI = confidence interval; € = Eurodollar; NA = not applicable; NR = not reported; WES = whole exome sequencing


Final


Only one study reported cost-effectiveness after a median follow-up of 473 days.13-16 In a 2017

publication from this study, authors reported a cost per QALY gained of AU$ -1,578 (95% CI,

AU$ -205,450 to AU$ 19,780) when only considering the changes in management that would

have occurred because of diagnosis for the proband. When considering changes in management

to the proband, cascade testing for first-degree relatives, and reproductive planning in relatives,

the cost per QALY gained was AU$ 8,119 (95% CI, AU$ 1,062 to AU$ 38,944). In a follow-up

publication to this same study, the authors modeled health outcomes over 20 years and reported

cost per QALY gained of AU$ 31,144 for management changes to probands only and AU$

14,235 when also considering cascade testing and reproductive outcomes in first-degree

relatives.

The other 4 studies reported heterogenous outcomes. Cordoba et al. reported the cost of

expendable diagnostic workup (i.e., workup that could be replaced by WES) was US$ 1,646.18

Dillon et al. reported that in 26% of the WES-diagnosed children for whom an available

commercial gene panel would have been diagnostic, the least costly panel had a higher price than

the price of WES.17 Nolan et al. reported that the average cost of initial and secondary testing

prior to WES was US$ 4,853 and if WES was performed after initial but prior to secondary

testing, an estimated savings of US$ 2,968 would have been observed.24 Lastly, Soden et al.

reported that for an average cost of prior testing of US$ 19,100 per family, WES would be cost-

effective at a price of US$ 2,996 per individual.25

Findings Among Single-Phenotype Populations

Cost-related outcomes from the 8 studies enrolling single-phenotype populations are reported in

Table 14. For most of the phenotypes evaluated in this evidence base, only one study was

available precluding definitive conclusions about cost for specific phenotypes. Because of

differences in currency and year reported across studies, we focus our synthesis on qualitative

differences among testing strategies. Overall, when compared to standard diagnostic pathways,

testing pathways that used WES identified more diagnoses at a lower cost in some studies,

identified more diagnoses but at a higher cost in other studies (range US$ 1,775 to US$ 8,559

higher depending on where WES was used in the testing pathway). And, testing with WES

earlier in the diagnostic pathway appeared to be associated with more cost savings or lower costs

per additional diagnosis compared to WES as a last resort strategy. The rest of this section

provides detailed findings.

Five studies compared a standard diagnostic pathway to one or more pathways that included

WES for Australian infants and children with epilepsy.27,28 Dutch children with intellectual and

developmental disability (IDD),80 Australian children with muscular dystrophy or nemaline

myopathy,26 and Australian adults and children with peripheral neuropathy. In 3 of these studies,

the cost per patient was less in the WES pathways compared to the standard pathways.26,27,80 In

the fourth study, the cost per patient was only less in the pathways that used WES early in the

diagnostic pathway, but not later in the pathway.28 In the fifth study, the cost per patient in the

standard diagnostic pathway was lower than in the pathways that used WES as a last resort and

slightly lower in the pathway that used WES early in the pathway.78


Final


Table 14. Summary of cost-related outcomes from studies enrolling single-phenotype populations



Currency; Year Cost of WES Cost Per Patient; Mean (95% CI)



Other Cost Outcomes

Howell (2018)28 Australia

86 Infants with severe epilepsy

US$; 2016 Singleton, targeted analysis: $1,639

Path 1 (Tier 1, Tier 2, Repeat MRI, Tier 3): $7,687 Path 2 (Tier 1, Tier 2, Repeat MRI, Tier 3, WES): $8,538 Path 3 (Tier 1, Tier 2, Repeat MRI, WES, Tier 3): $8,027 Path 4 (Tier 1, Tier 2, WES, Repeat MRI, Tier 3): $8,069 Path 5 (Tier 1, WES, Tier 2, Repeat MRI, Tier 3): $7,873 Path 6 (Tier 1, WES, Repeat MRI, Tier 2): $6,453 Path 7 (Tier 1, WES, Repeat MRI): $5,298

Path 1: $16,951 Path 2: $15,378 Path 3: $14,382 Path 4: $14,457 Path 5: $14,106 Path 6: $11,530 Path 7: $9,904

Compared to Path 1: Path 2: $8,559 Path 3: $3,250 Path 4: $3,650 Path 5: $1,775 Path 6: Dominates (i.e., identified more diagnoses at lower cost) Path 7: Dominates

NR

Monroe (2016)80 Netherlands

17 children with IDD from a tertiary specialty clinic

US$; 2014 Trio: $3,972 Median (range) Traditional path: $14,153 ($6,343 to $47,841) Median (range) cost savings from early WES: Diagnosed participants: $5,342 ($0 to $10,684) Undiagnosed participants: $4,854 ($890 to $18,696)

NR NR NR

Palmer (2018)27 Australia

30 children with infantile-onset epileptic encephalopathy

AU$; NR Trio: $4,036 to $12,362 (varied by commercial lab)

Standard path: $11,827 ($10,677 to $13,027) WES path: $9,536 ($9,412 to $9,683)

Mean (95% CI) Standard path: $182,243 ($72,703 to $406,142) WES path: $19,074 ($14,421 to $27,969)

Compared to standard path: WES path: $-5,236 ($-2,483 to $-9,784)

NR

Schofield (2017)26 Australia

56 children with congenital muscular dystrophy or nemaline myopathy

AU$; 2016 Singleton: $1,718

Mean (95% CI) Traditional path: $10,491 ($9,115 to $11,848) Neuromuscular gene path: $3,808 ($3,293 to $4,373) WES path: $6,077 ($5,284 to $6,846)

Mean (95% CI) Traditional path: $22,596 ($17,004 to $31,498) Neuromuscular gene path: $5,077 ($4,228 to $6,100) WES path: $7,734 ($6,166 to $9,696)

Compared to the traditional pathway Neuromuscular gene pathway: $-23,390 ($-14,595 to $-41,184) WES path: $-13,732 ($-7,938 to $-473)

NR


Final


Table 14. Summary of cost-related outcomes from studies enrolling single-phenotype populations (continued)



Currency; Year Cost of WES Cost Per Patient; Mean (95% CI)



Other Cost Outcomes

Tsiplova (2017)79 Canada

Synthetic modeling population for children with autism

CA$; 2015 Singleton; $1,655

Per sample CMA: $744 ($714 to $773) CMA + WES: $1,655 ($1,611 to $1,699)

NR Compared to CMA alone: CMA + WES: $25,458

NR

Vrijenhoek (2018)77 Netherlands

370 children with intellectual disabilities

€; NR Trio; €3,600 NR NR NR Costs after WES as last test in diagnostic trajectory: 82% lower than before testing Costs after WES as first-tier test: 58% lower than before testing

Walsh (2017)78 Australia

50 adults and children with peripheral neuropathy

AU$; NR Singleton; $2,000

Standard investigations: $4,013 (SD $2,761) Standard investigations and WES as last resort: $6,344 Early WES: $4,914

Standard investigations and WES as last resort strategy: $16,027 Early WES: $12,413

Compared to standard investigations alone: WES as last resort strategy: $5,889 Early WES: $2,276

NR

Abbreviations: AU$ = Australian Dollar; CI = confidence interval; CMA = chromosomal microarray analysis; NR = not reported; WES = whole exome sequencing


Final


In the 4 studies that reported cost per diagnosis, costs were all less in the WES pathways

compared to the standard diagnostic pathways26-28 or in the early WES pathways compared to

WES as a last resort.78 In 2 studies reporting cost per additional diagnosis, authors reported a cost

savings in the WES path compared to the standard diagnostic pathway.26,27 In other words, the

WES pathway identified more diagnoses than the standard pathway at a lower cost. In the third

study reporting cost per additional diagnosis, the pathways evaluating WES early in the

diagnostic pathway also demonstrated cost savings.28 In that same study, the cost per additional

diagnosis for pathways involving WES later in the diagnostic pathway ranged from US$ 1,775 to

US$ 8,559 when compared to the standard pathway. In the fourth study, the cost per additional

diagnosis ranged from US$ 2,276 (early WES) to US$ 5,889 (last resort WES).78

In the study conducted among children with muscular dystrophy or myopathy, a third diagnostic

pathway involved a neuromuscular gene panel (464 genes) was also evaluated.26 This pathway

had a lower cost per patient and cost per diagnosis compared to both the standard diagnostic

pathway and the WES pathway. In addition, compared to the standard pathway, the

neuromuscular gene panel pathway demonstrated higher cost savings than the WES pathway.

The other 2 studies reported heterogenous outcomes. Tsiplova et al., a modeling study of

Canadian children with autism, estimated that the cost per additional diagnosis for a strategy of

CMA plus WES compared to CMA alone was CA$ 25,458.79 Vrijenhoek et al., a study among

Dutch children with IDD, estimated that health care costs after WES as a last resort strategy were

82% lower than before WES testing, and were 58% lower than before testing if used as a first-

tier test.77

4. Discussion

4.1 Summary of the Evidence

Our assessment of the evidence from the contextual questions confirmed that WES has a higher

diagnosis yield compared to standard testing pathways and phenotype-specific gene panels.

Among all phenotypes, we calculated the pooled diagnostic yield for WES as 38%, which is

higher than the pooled diagnostic yield for traditional testing pathways and gene panels.

Reanalysis of WES data using updated variant call algorithms and newly discovered

pathogenicity information increases diagnostic yield on average by about 17%. Because this was

a contextual question, we did not assess the certainty of the evidence.

The findings from the key questions and certainty of evidence is summarized in Figure 4.


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Figure 4. Summary of evidence from whole exome sequencing HTA

A molecular diagnosis with WES changed medical management for 12% to 100% of diagnosed

patients among diverse populations and for 0% to 31% of diagnosed patients with epilepsy.

Medication was changed for 5% to 25% of patients who received a diagnosis from WES.

Additional counseling or testing of family members occurred in between 4% and 97% of families

who received a diagnosis with WES. The certainty of the evidence was very low.

Patients who received a diagnosis from WES had higher mortality than those who remained

undiagnosed, and infants that received rapid WES had higher mortality than those who received

standard WES. Differences in the time period of reported mortality rates precluded our

calculation of pooled estimates. No study controlled for baseline differences and, in some studies

of rapid WES, rapid WES was performed on the sickest infants. Among patients with epilepsy,

management changes resulting from WES diagnosis improved behavior or seizure control in 1%

and 3% of participants, respectively. Due to differences in study design and reported outcomes,

we were unable to evaluate the certainty of the evidence regarding health outcomes.


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We found little evidence of safety issues related to WES testing. We calculated the pooled

percent of patients with an ACMG-defined medically actionable variant as 3.9%. We rated the

certainty of evidence on the frequency of ACMG-defined medically actionable variants as low.

In the 1 reported case in which WES results led to discovery of unexpected family relationships,

the participant experienced only mild shock. Very few patients or parents of patients who

received negative or uncertain WES results experienced psychosocial harms from the test results.

We did not rate the certainty of these findings because this evidence was primarily qualitative.

None of the studies we identified assessed harms from misdiagnosis due to misclassification of a

benign variant as pathogenic. Such misdiagnosis could result in harmful or ineffective treatment,

inappropriate testing of family members, and failure to identify the correct diagnosis.

In both single-phenotype and diverse-phenotype populations, testing pathways that included

WES identified more diagnoses and ranged from either costing less or costing somewhat more

(the highest reported estimate was US$ 8,559 per additional diagnosis) compared to a standard

diagnostic pathway. Pathways with earlier WES testing were more likely to have cost savings

than pathways that used WES later in the testing pathway or as a last-resort strategy. WES test

costs reported in studies ranged from $1,000 to $15,000; we found that costs reported for trio

WES are higher than those for singleton WES. The certainty of the evidence on the cost and

cost-effectiveness of WES was very low.

4.2 Limitations of the Evidence Base

The body of evidence on WES has substantial limitations. There are few prospective studies that

have collected standardized data on clinical-utility or health outcomes. Most studies were

retrospective and collected data solely from medical records. Few studies described protocols for

data abstraction or approaches to ensure standardized, accurate, and replicable abstraction. Some

studies explicitly excluded subjects for which they were unable to obtain outcomes data, which

introduced selection bias. Other studies did not report on how they handled subjects with missing

records or data.

Few studies included a comparison group; therefore, we could only estimate the frequency of

outcomes within a single group. We were unable to compare the clinical utility or health impact

of WES to that of other genetic testing methods. Most studies are small, single-center studies

with heterogenous study populations. As such, it is difficult to compare study outcomes among

the studies, and likely that the results would have been different with a different patient mix. The

clinical trials focused only on diagnostic yield between rapid WES and standard WES. Studies

that are not favorable to WES may not be published. We were unable to evaluate the extent of

publication bias in the body of evidence because these studies are not typically registered in trial

registries.

The complexity and rapid evolution of WES further complicates its evaluation. The technology

continues to change rapidly, which hinders the ability to determine the applicability of studies

from just a few years ago. It is also challenging to evaluate how sequencing platforms,

bioinformatics approaches, or testing approaches may affect the findings of individual studies.


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The nature of WES testing makes well-designed comparative effectiveness studies complicated.

WES can diagnosis a wide range of conditions—many with very similar phenotypes but very

different underlying genetic diagnoses with drastically different recommended management

strategies and outcomes. It is difficult to determine the most appropriate comparison group, and

given the interpatient variability, very large sample sizes would be required to ensure precise

measurement. Although randomized-controlled trials that use rigorous data collection and

outcome measurement could be designed in order to produce results with a high degree of

certainty under GRADE, they are likely not feasible to conduct in practice. Such trials would

require large, multisite networks to be able to include enough patients with similar phenotypes

and would need to follow up participants over years.

4.3 Clinical Practice Guidelines and Related Health Technology

Assessments

We did not identify any clinical practice guideline specific to diagnostic testing with WES. We

identified 4 HTAs cataloged in the University of York’s Centre for Reviews and Dissemination

of the National Institute for Health Research in the United Kingdom. Two of these assessments

(i.e., Hayes, Inc. and Blue Cross/Blue Shield) require a subscription to access.81,82 The other 2

HTAs were produced in the Netherlands and Argentina and are not available in English.83,84

We identified 1 narrative review from the “Model Coverage Policies” page on the American

Academy of Neurology’s (AAN’s) website.85 This document includes suggested indications and

contraindications for exome sequencing, which are detailed in Table 15.

Table 15. Indications and contraindications for clinical exome sequencing from the American Academy of Neurology’s Model Coverage Policies85

Indications

Undiagnosed neurologic disorder with nonspecific or clinically heterogenous phenotype

Expert evaluation with detailed clinical history, comprehensive neurologic examination, and complete family history

Complete evaluation for common causes that do not require genetic testing

Negative initial genetic testing (e.g., high-yield, single-gene, or multigene testing; chromosomal microarray) based on clinical evaluation, as appropriate

Contraindications

Exome sequencing is not to be considered as a primary or first-line test for establishing a diagnosis in a patient when a genetic disorder is suspected unless the indications criteria are met.

Testing is not to be carried out without prior clinical evaluation and confirmation of need by appropriately trained professional health care providers with experience in the diagnostic evaluation of genetic disease.

Testing is not to be carried out without careful consideration, appropriate genetic counseling (including discussion of the possibility of secondary or incidental findings), and the availability of clinical expertise to interpret the findings, render advice, and provide appropriate care and management decisions based on the results of the testing.

We identified 6 documents produced by the ACMG. Two of these documents, published in 2013

and 2016, specify recommendations for reporting on incidental findings.2,91 One document

published in 2015 specifies standards and guidelines for the interpretation of sequence

variants.103 A policy statement published in 2012, “Points to Consider in the Clinical Application

of Genomic Sequencing,”86 describes indications for testing, which are listed in Table 16.


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Table 16. Indications for diagnostic testing from 2012 policy statement entitled “Points to Consider in the Clinical Application of Genomic Sequencing”86

WGS/WES should be considered in the clinical diagnostic assessment of a phenotypically affected individual when: e. The phenotype of family history data strongly implicate a genetic etiology, but the phenotype does not correspond

with a specific disorder for which a genetic test targeting a specific gene is available on a clinical basis. f. A patient presents with a defined genetic disorder that demonstrates a high degree of genetic heterogeneity, making

WES or WGS analysis of multiple genes simultaneously a more practical approach. g. A patient presents with a likely genetic disorder but specific genetic tests available for that phenotype have failed to

arrive at a diagnosis. h. A fetus with likely genetic disorder in which specific genetic tests, including targeted sequencing test, available for

that phenotype have failed to arrive at a diagnosis. j. Prenatal diagnosis by genomic (i.e., next generation whole exome or whole genome) sequencing has

significant limitations. The current technology does not support short-turnaround times which are often expected in the prenatal setting. There are high false positive, false negative, and variants of unknown clinical significance rates.

Abbreviations: WES = whole exome sequencing; WGS = whole genome sequencing

The last ACMG document we identified provides guidance about the reevaluation and reanalysis

of genomic test results.104 This document describes considerations for variant-level reevaluation,

case-level reanalysis, and retesting. It describes general considerations but does not provide a

specific timeframe for considering reanalysis.

4.4 Selected Payer Coverage Policies

Specific payor coverage policies for WES are detailed in Table 17. CMS does not have a

national coverage determination for WES. Five commercial payers cover WES when

beneficiaries have met specific clinical criteria.


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Table 17. Payer coverage policies for whole exome sequencing

Payer; Effective Date Policy

Aetna105 May 16, 2019

Whole exome sequencing (WES) is considered medically necessary for the evaluation of unexplained congenital or neurodevelopmental disorder in children </= 21 years of age when all of the following criteria are met:

A. A genetic etiology is considered the most likely explanation for the phenotype, based on either of the following: 1. Multiple congenital abnormalities affecting unrelated organ systems; or 2. Two of the following criteria are met:

i. Abnormality affecting at minimum a single organ system (e.g., brain), ii. Significant developmental delay, intellectual disability (e.g., characterized by significant limitations in both intellectual functioning and in adaptive behavior),

symptoms of a complex neurodevelopmental disorder (e.g., self-injurious behavior, reverse sleep-wake cycles, dystonia, hemiplegia, spasticity, epilepsy, muscular dystrophy), and/or severe neuropsychiatric condition (e.g., schizophrenia, bipolar disorder, Tourette syndrome),

iii. Family history strongly suggestive of a genetic etiology, including consanguinity, iv. Period of unexplained developmental regression, v. Biochemical findings suggestive of an inborn error of metabolism, and

B. The member and family history have been evaluated by a Board-Certified or Board-Eligible Medical Geneticist, and C. Member receives pre- and post-test counseling by an appropriate independent provider (not an employee of the genetics testing laboratory), such as an American

Board of Medical Genetics or American Board of Genetic Counseling-certified Genetic Counselor, or an Advanced Practice Nurse in Genetics (APGN) credentialed by either the Genetic Nursing Credentialing Commission (GNCC) or the American Nurses Credentialing Center (ANCC), and

D. Alternate etiologies have been considered and ruled out when possible (e.g., environmental exposure, injury, infection), and E. Clinical presentation does not fit a well-described syndrome for which single-gene or targeted panel testing is available, and F. WES is more efficient than the separate single-gene tests or panels that would be recommended based on the differential diagnosis (e.g., genetic conditions that

demonstrate a high degree of genetic heterogeneity), and G. A diagnosis cannot be made by standard clinical workup, excluding invasive procedures such as muscle biopsy, and H. WES is predicted to have an impact on health outcomes, including:

1. Guiding prognosis and improving clinical decision-making, which can improve clinical outcome by: i. application of specific treatments as well as withholding of contraindicated treatments for certain rare genetic conditions, ii. surveillance for later-onset comorbidities, iii. initiation of palliative care, iv. withdrawal of care; or

2. Reducing diagnostic uncertainty (e.g., eliminating lower-yield testing and additional screening testing that may later be proven unnecessary once a diagnosis is achieved); or

3. For persons planning a pregnancy, informing genetic counseling related to recurrence risk and prenatal diagnosis options; and I. Family trio testing (whole exome sequencing of the biologic parents or sibling of the affected child) is considered medically necessary when criteria for whole

exome sequencing of the child are met.


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Table 17. Payer coverage policies for whole exome sequencing for any indication (continued)


Cigna106 December 15, 2018

Whole exome sequencing is considered medically necessary when disease-specific criteria* listed below are met and when a recommendation for testing is confirmed by ONE of the following:

An independent Board-Certified or Board-Eligible Medical Geneticist An American board of Medical Genetics or American Board of Genetic Counseling- certified Genetic Counselor not employed by a commercial genetic

testing laboratory (Genetic counselors are not excluded if they are employed by or contracted with a laboratory that is part of an Integrated Health System which routinely delivers health care services beyond just the laboratory test itself).

A genetic nurse credentialed as either a Genetic Clinical Nurse (GCN) or an Advanced Practice Nurse in Genetics (APGN) by either the Genetic Nursing Credentialing Commission (GNCC) or the American Nurse Credentialing Center (ANCC) who is not employed by a commercial genetic testing laboratory. (Genetic nurses are not excluded if they are employed by or contracted with a laboratory that is part of an Integrated Health System which routinely delivers health care services beyond just the laboratory test itself).

WHO: Has evaluated the individual Completed a three generation pedigree Intends to engage in post-test follow-up counseling

*Disease Specific Criteria Whole exome sequencing (CPT code 81415) is considered medically necessary for a phenotypically affected individual when ALL of the following criteria are met:

Individual has been evaluated by a board-certified medical geneticist or other board-certified specialist physician specialist with specific expertise in the conditions and relevant genes for which testing is being considered

WES results will directly impact clinical decision-making and clinical outcome for the individual being tested A genetic etiology is the most likely explanation for the phenotype as demonstrated by ANY of the following:

o Multiple abnormalities affecting unrelated organ systems o Known or suspected early infantile epileptic encephalopathy (onset before three years of age) o TWO of the following criteria are met

Abnormality affecting a single organ system Significant intellectual disability, symptoms of a complex neurodevelopmental disorder (e.g. self-injurious behavior, reverse sleep-wake

cycles), or severe neuropsychiatric condition (e.g. schizophrenia, bipolar disorder, Tourette syndrome) Family history strongly implicating a genetic etiology Period of unexplained developmental regression (unrelated to autism or epilepsy)

No other causative circumstances (e.g. environmental exposures, injury, infection) can explain symptoms Clinical presentation does not fit a well-described syndrome for which single-gene or targeted panel testing (e.g., comparative genomic hybridization

(CGH)/chromosomal microarray analysis [CMA], is available


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Cigna December 15, 2018

The differential diagnosis list and/or phenotype warrant testing of multiple genes and ONE of the following: o WES is more practical than the separate single gene tests or panels that would be recommended based on the differential diagnosis o WES results may preclude the need for multiple and/or invasive procedures, follow=up, or screening that would be recommended in the absence

of testing Comparator exome sequence analysis (CPT code 81416) is considered medically necessary when the above criteria for WES (CPT code 81415) have been met and WES is being performed concurrently or has been previously performed. Experimental/Investigational/Unproven Prenatal diagnosis or preimplantation testing of an embryo using WES is considered experimental, investigational, and unproven. WES in the general population is considered not medically necessary.

Humana107 July1, 2019

Any state mandates for WGS, exome sequencing or GWAS take precedence over this clinical policy. Genetic testing may be excluded by contract. Please consult the member’s individual contract, regarding Plan coverage. Humana members many NOT be eligible under the Plan for the following:

Exome sequencing including the following: o Custom exome panels (e.g., XomeDXSlice, XomeDx Slice Xpanded) for single-gene or multigene panels; OR o EXaCT-1 Whole Exome Testing; OR o Trio whole exome sequencing; OR

Genome wide association studies (GWAS); OR

Mate-pair sequencing (i.e., MatePair, Targeted Rearrangements, Oncology; MatePair, Targeted Rearrangements; Hematologic); OR

Testing an at-risk (unaffected) individual or affected individual when a family member has been tested for mutations and received a result of VUS (also known as unclassified variant or variant of uncertain significance); OR

Whole genome sequencing (WGS) including RCIGM Rapid WGS These are considered experimental/investigational as they are not identified as widely used and generally accepted for the proposed uses as reported in nationally recognized peer-reviewed medical literature published in the English language.


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Humana July 1, 2019

Medical Alternatives to WGS, exome sequencing or GWAS include, but may not be limited to, the following:

Chromosomal microarray analysis

Fluorescent situ hybridization (FISH)

Standard cytogenetic testing (e.g., karyotype)

Targeted mutation analysis consistent with personal and family histories Physician consultation is advised to make an informed decision based on an individual’s health needs.

Kaiser Permanente (Washington)108 NR

Whole exome sequencing (WES) is considered medically necessary for a phenotypically affected individual when ALL of the following criteria are met: 1. Individual has been evaluated by a board-certified medical geneticist (MD) or other board-certified physician specialist with specific expertise in the conditions and

relevant genes for which testing is being considered 2. Results have the potential to directly impact clinical decision-making and clinical outcomes for the patient 3. A genetic etiology is the most likely explanation for the phenotype as demonstrated by EITHER of the following:

A. multiple abnormalities affecting unrelated organ systems OR B. TWO of the following criteria are met:

a. abnormality affecting a single organ system b. significant intellectual disability, symptoms of a complex neurodevelopmental disorder (e.g. self-injurious behavior, reverse sleep-wake cycles) or severe

neuropsychiatric condition (e.g., schizophrenia, bipolar disorder, Tourette syndrome) c. family history strongly implicating a genetic etiology d. period of unexplained developmental regression (unrelated to autism or epilepsy) e. dysmorphic facial features f. abnormal growth not otherwise explained

4. No other causative circumstances (e.g. environmental exposures, injury, infections) can explain symptoms 5. Clinical presentation does not fit a well-described syndrome for which single-gene or targeted panel testing is available 6. The differential diagnosis list and/or phenotype warrant testing of multiple genes and ONE of the following:

A. WES is more practical than the separate single-gene tests or panels that would be recommended based on the differential diagnosis B. WES results may precede the need for multiple and/or invasive procedures, follow-up , or screening that would be recommended in the absence of testing

All requests must be approved by a KP geneticist, regardless of whether they have seen the patient.


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Premera (Blue Cross)109 January 4, 2019

This payor defers to the clinical appropriateness guidelines entitled “Whole Exome and Whole Genome Sequencing” published by AIM Specialty Health (version dated March 31, 2019). Whole exome sequencing (WES) is medically necessary for a phenotypically affected individual when all of the following criteria are met:

Individual has been evaluated by a board-certified medical geneticist or other board-certified specialist physician with specific expertise in the conditions being tested for and relevant genes

WES results will directly impact clinical decision-making and/or clinical outcome

A genetic etiology is the most likely explanation for the phenotype as demonstrated by one of the following: Multiple abnormalities affecting unrelated organ systems Known or suspected infantile or early-onset epileptic encephalopathy (onset before three years of age) for which likely non-genetic causes of epilepsy (e.g.

environmental exposures; brain injury secondary to complications of extreme prematurity, infection trauma) have been excluded Or two of the following four criteria:

Abnormality affecting a single organ system

Significant intellectual disability or severe psychological/ psychiatric disturbance (e.g. self-injurious behavior, reversed sleep-wake cycles)

Family history strongly implicating a genetic etiology

Period of unexplained developmental regression (unrelated to autism or epilepsy)

No other causative circumstances (e.g. environmental exposures, injury, infection) can explain symptoms

Clinical presentation does not fit a well-described syndrome for which single-gene or targeted panel testing is available

The differential diagnosis list and/or phenotype warrant testing of multiple genes, and at least one of the following: WES is more practical than the separate single gene tests or panels that would be recommended based on the differential diagnosis WES results may preclude the need for multiple and/or invasive procedures, follow-up, or screening that would be recommended in the absence of testing

Prenatal diagnosis or preimplantation testing of an embryo using WES is not medically necessary. WES for the purpose of genetic carrier screening is not medically necessary

Whole Exome Reanalysis

Reanalysis of previously obtained uninformative whole exome sequencing is medically necessary when one of the following criteria is met: There has been onset of additional symptoms that broadens the phenotype assessed during the original exome evaluation There has been the birth or diagnosis of a similarly affected first-degree relative that has expanded the clinical picture.

Regence (Blue Shield)110 July 1, 2019

WES and whole genome sequencing is considered investigational for all indications, including but not limited to, diagnosis in patients with suspected genetic disorders, preimplantation or prenatal (fetal) testing, and general screening.


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United HealthCare Commercial111 October 1, 2019

Genetic counseling is strongly recommended prior to these tests in order to inform persons being tested about the advantages and limitations of the test as applied to a unique person. WES is proven and medically necessary for diagnosing or evaluating a genetic disorder when the results are expected to directly influence medical management and clinical outcomes and ALL of the following are met:

Clinical presentation is nonspecific and does not fit a well-defined syndrome for which a specific or targeted gene test is available. If a specific genetic syndrome is suspected, a single gene or targeted gene panel should be performed prior to determining if WES is necessary; and

WES is ordered by a board-certified medical geneticist, neonatologist, neurologist, or developmental and behavioral pediatrician; and one of the following:

The clinical presentation or clinical and family history strongly suggest a genetic cause for which a specific clinical diagnosis cannot be made with any clinically available targeted genetic tests; or

There is a clinical diagnosis of a genetic condition where there is significant genetic heterogeneity and WES is a more practical approach to identifying the underlying genetic cause than are individual tests of multiple genes; or

There is likely a genetic disorder and multiple targeted gene tests that have failed to identify the underlying cause Comparator (e.g., parents or siblings) WES is proven and medically necessary for evaluating a genetic disorder when the above criteria have been met and WES is performed concurrently or has been previously performed on the individual. WES is unproven and not medically necessary for all other indications, including but not limited to the following:

Screening and evaluating disorders in individuals when the above criteria are not met

Prenatal genetic diagnosis or screening

Evaluation of fetal demise

Preimplantation Genetic Testing (PGT) in embryos

Molecular profiling of tumors for the diagnosis, prognosis or management of cancer Further studies are needed to evaluate the clinical utility of whole exome sequencing for other indications.

Abbreviations: PGT = preimplantation genetic testing; WES = whole exome sequencing


Final


4.5 Limitations of This HTA

This HTA was limited to peer-reviewed studies published in English. Our search was limited to 3

bibliographic databases; however, we conducted extensive hand searches to identify potentially

relevant articles. Because of practical constraints, our key questions focused on clinical utility

outcomes, health outcomes, safety outcomes, and cost outcomes. We did not systematically

review studies of diagnostic yield. However, we provided information about diagnostic yield

based on 4 systematic reviews and 99 primary research studies that we identified as having

relevant diagnostic yield information during full-text screening.

We applied the standard GRADE approach to assessing the certainty of evidence for the various

outcomes we included; however, this framework may not be well suited for assessing the

strength of evidence for genetic testing as it was initially developed with a focus on RCTs of

therapeutic interventions. We considered secondary findings resulting from WES testing as part

of the safety key question because such findings do not directly impact the clinical utility or

health outcomes related to the phenotype for which the patient is being tested. However, we

acknowledge that some medically actionable secondary findings may improve health outcomes

unrelated to the patient’s phenotype. However, such outcomes were outside of the scope of this

review.

4.6 Ongoing Research

We identified 15 recently completed or ongoing studies that may be relevant to this topic (Table

18). Most are single-arm observational cohorts. The only ongoing RCT that we identified is

sponsored by the University of North Carolina at Chapel Hill in collaboration with the National

Human Genome Research Institute and 2 other North Carolina-based health care systems.87 In

this RCT, children and adults with diverse phenotypes are randomized to 1 of 4 study arms 1)

previsit preparation with usual care and exome sequencing, 2) previsit preparation with usual

care, 3) no previsit prep with exome sequencing, and 4) no previsit prep and usual care. This

study plans to enroll 1,700 participants with an estimated study completion date of May 2021.

The trial registry record lists 22 primary care outcome measures including various measures of

health care utilization, patient and caregiver quality of life.

Table 18. Summary of ongoing whole exome sequencing studies

Registration Number

Sponsor Study Designed

Title Number of Participants

Status Estimated Completion Date

NCT02862808112 Centre Hospitalier Universitaire de Besancon

Retrospective observational cohort

Molecular Diagnosis of Syndromic or Isolated Severe Intellectual Disability Using Whole Exome Sequencing: a Pilot Study.

17 Recruiting 9/2019

NCT03721458113 Milton S. Hershey Medical Center


Whole Genome Sequencing in the Neonatal Intensive Care Unit



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Table 18. Summary of ongoing whole exome sequencing studies (continued)

Registration Number

Sponsor Study Designed

Title Number of Participants

Status Estimated Completion Date

NCT03890679114 Tufts Medical Center

Single-arm trial Genomic Medicine for Ill Neonates and Infants (The GEMINI Study) (GEMINI)


NCT02380729115 Charite University, Berlin, Germany

Prospective observational cohort

Mutation Exploration in Non-acquired, Genetic Disorders and Its Impact on Health Economy and Life Quality (MENDEL)

200 Completed 12/2017

NCT03287193116 Centre Hospitalier Universitaire Dijon


Identification of the Molecular and/or Pathophysiological Bases of Developmental Diseases (DISCOVERY)




Medico-economic Evaluation of Different High-throughput Sequencing Strategies in the Diagnosis of Patients With Intellectual Deficiency (DISSEQ)



Non-randomized clinical trial

Secondary Findings From High-throughput Sequencing: How to Announce Them With Respect to the Patient's Needs (FIND)


NCT02769975119 Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

Observational cohort

Evaluation of Children With Endocrine and Metabolic-Related Conditions

15,000 Recruiting 12/2030

NCT03175692120 National Taiwan University Hospital

Cross-sectional observational cohort

Rapid Genetic Diagnosis Employing Next Generation Sequencing for Critical Illness in Infants and Children


NCT02077894121 National Eye Institute (NEI)


Whole Exome and Whole Genome Sequencing for Genotyping of Inherited and Congenital Eye Conditions


NCT03605004122 National Human Genome Research Institute (NHGRI)


Adult Patients With Undiagnosed Conditions and Their Responses to Clinically Uncertain Results From Exome Sequencing


NCT02699190123 Children's Hospital of Philadelphia

Observational cohort

LeukoSEQ: Whole Genome Sequencing as a First-Line Diagnostic Tool for Leukodystrophies


NCT02995538124 University of Pittsburgh

Patient registry Neurogenetics Patient Registry 1,000 Recruiting 1/2028

NCT03525431125 University of California, San Francisco

Single-arm trial Clinical Utility of Pediatric Whole Exome Sequencing


NCT0354877987 University of North Carolina, Chapel Hill

Randomized controlled trial

North Carolina Genomic Evaluation by Next-generation Exome Sequencing, 2 (NCGENES2)

1,700 Recruiting 5/2021


Final


5. Conclusion

WES increases the yield of molecular diagnosis over standard diagnostic testing. A diagnosis

from WES changes clinical management for some patients, but our certainty in the estimate of

this frequency is very low. The evidence regarding the impact of WES testing on health and most

safety outcomes is limited, though we have low certainty that the proportion of patients tested

who receive a medically actionable secondary finding is about 3.9%. WES may be cost-effective

in terms of diagnostic success, but our certainty is very low. Testing pathways that included

WES identified more diagnoses and either cost less or cost somewhat more than a standard

diagnostic pathway. Pathways with earlier WES testing were more likely to have cost savings

than pathways that used WES later in the testing pathway or as a last-resort strategy.

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Final


provider.kaiserpermanente.org/static/pdf/hosting/clinical/criteria/pdf/genetic_screening.p

df. Published 2016. Updated Last update date not reported. Accessed August 26, 2019.

109. Premera Blue Cross. Administrative Guideline 10.01.526 Molecular Genetic Testing:

Services Reviewed by AIM.

http://www.aimspecialtyhealth.com/PDF/Guidelines/2019/Mar31/WholeExomeandWhol

eGenomeSequencing.pdf. Published 2019. Updated January 4, 2019. Accessed August

26, 2019.

110. Regence Blue Shield. Medical policy manual. Whole Exome and Whole Genome

Sequncing, Policy No. 76. http://blue.regence.com/trgmedpol/geneticTesting/gt76.pdf.

Published 2019. Updated July 1, 2019. Accessed August 26, 2019.

111. UnitedHealthCare. Medical Policy CS150.F Whole Exome and Whole Genome

Sequencing.

https://www.uhcprovider.com/content/dam/provider/docs/public/policies/medicaid-

comm-plan/whole-exome-whole-genome-sequencing-cs.pdf. Published 2019. Updated

October 1, 2019. Accessed October 15, 2019.

112. Centre Hospitalier Universitaire de Besancon. Molecular diagnosis of syndromic or

isolated severe intellectual disability using whole exome sequencing : a pilot study.

https://ClinicalTrials.gov/show/NCT02862808. Published 2017. Updated September 20.

113. Milton S. Hershey Medical Center. Whole genome sequencing in the neonatal intensive

care unit. https://ClinicalTrials.gov/show/NCT03721458. Published 2019. Updated May

1.

114. Tufts Medical Center. Genomic medicine for ill neonates and infants (The GEMINI

Study). https://ClinicalTrials.gov/show/NCT03890679. Published 2019. Updated March

21.

115. Charite University; German Federal Ministry of Education Research. Mutation

exploration in non-acquired, genetic disorders and its impact on health economy and life

quality. https://ClinicalTrials.gov/show/NCT02380729. Published 2015. Updated January

31.

116. Centre Hospitalier Universitaire Dijon. Identification of the molecular and/or

pathophysiological bases of developmental diseases.

https://ClinicalTrials.gov/show/NCT03287193. Published 2017. Updated March 13.

117. Centre Hospitalier Universitaire Dijon. Medico-economic evaluation of different high-

throughput sequencing strategies in the diagnosis of patients with intellectual deficiency.

https://ClinicalTrials.gov/show/NCT03287206. Published 2017. Updated June 28.

118. Centre Hospitalier Universitaire Dijon. Secondary findings from high-throughput

sequencing: how to announce them with respect to the patient's needs.

https://ClinicalTrials.gov/show/NCT03288727. Published 2017. Updated November 4.

119. Eunice Kennedy Shriver National Institute of Child Health. Evaluation of children with

endocrine and metabolic-related conditions.

https://ClinicalTrials.gov/show/NCT02769975. Published 2016. Updated May 11.

120. National Taiwan University Hospital. Rapid genetic diagnosis employing next generation

sequencing for critical illness in infants and children.

https://ClinicalTrials.gov/show/NCT03175692. Published 2017. Updated June 14.

121. National Eye Institute. Whole exome and whole genome sequencing for genotyping of

inherited and congenital eye conditions. https://ClinicalTrials.gov/show/NCT02077894.

Published 2014. Updated February 28.



http://www.aimspecialtyhealth.com/PDF/Guidelines/2019/Mar31/WholeExomeandWholeGenomeSequencing.pdf

http://www.aimspecialtyhealth.com/PDF/Guidelines/2019/Mar31/WholeExomeandWholeGenomeSequencing.pdf

http://blue.regence.com/trgmedpol/geneticTesting/gt76.pdf

https://www.uhcprovider.com/content/dam/provider/docs/public/policies/medicaid-comm-plan/whole-exome-whole-genome-sequencing-cs.pdf

https://www.uhcprovider.com/content/dam/provider/docs/public/policies/medicaid-comm-plan/whole-exome-whole-genome-sequencing-cs.pdf

https://clinicaltrials.gov/show/NCT02862808











Final


122. National Human Genome Research Institute. Adult patients with undiagnosed conditions

and their responses to clinically uncertain results from exome sequencing.

https://ClinicalTrials.gov/show/NCT03605004. Published 2019. Updated April 12.

123. Children's Hospital of Philadelphia. LeukoSEQ: whole genome sequencing as a first-line

diagnostic tool for leukodystrophies. https://ClinicalTrials.gov/show/NCT02699190.

Published 2017. Updated January 6.

124. University of Pittsburgh. Neurogenetics patient registry.

https://ClinicalTrials.gov/show/NCT02995538. Published 2017. Updated January 30.

125. University of California, San Francisco. Clinical utility of pediatric whole exome

sequencing. https://ClinicalTrials.gov/show/NCT03525431. Published 2017. Updated

August 1.






Final

Whole exome sequencing: final evidence report Page A-1

Appendix A. State of Washington Health Care Authority

Utilization Data

Populations

The Whole Exome Sequencing (WES) analysis combined member utilization and cost data from

the following Washington agencies: Medicaid Managed Care (MCO) and Medicaid Fee-for-

Service (FFS).

The Department of Labor and Industries (LNI) Workers’ Compensation Plan reported no WES

utilization. The Public Employees Benefit Board Uniform Medical Plan (PEBB/UMP) reported

less than the minimum number of individuals necessary to safely release agency-by-agency

findings and still protect patient confidentiality.

Population inclusion criteria specified incurring at least one CPT 81415-claim line, with or

without a concurrent 81416 CPT(s) or having a standalone 81417 CPT (see Appendix A-Table

1). The data process involved extracting all WES claims; however, denied claims received a

separate analysis and were not included in utilization counts. The analysis period contained 4

calendar years of claims data from 2015 through 2018. All chart and graph analyses is by

calendar year.

Methods

Initial criteria identified all WES CPTs (see Appendix A-Table 1). Next, we obtained patient

claims containing the targeted WES CPTs along with a WES date of service. Next, we sorted

claims into 2 groupings: paid claims or denied claims. The paid claims grouping required

extraction of all paid claims incurred on a patient’s WES date of service. Data evaluation

involved examining utilization by member; analysis of individual and aggregate CPT codes by

age and calendar year; and by total claims’ costs incurred by a member on the date of their

service. Denied claims received a separate analysis for volume of denials.

Appendix A-Table 1. Targeted CPT - Whole Exome Sequencing

CPT Procedure Code Description

81415 Exome (e.g., unexplained constitutional or heritable disorder or syndrome); sequence analysis

Test conducted on the individual under study (IUS).

81416

Exome (e.g., unexplained constitutional or heritable disorder or syndrome); sequence analysis, each comparator exome (e.g., parents, siblings) (List separately in addition to code for primary procedure)

Tests conducted on the parents, siblings, etc. of the IUS. Codes attributed to the IUS.

81417

Exome (e.g., unexplained constitutional or heritable disorder or syndrome); reevaluation of previously obtained exome sequence (e.g., updated knowledge or unrelated condition/ syndrome)

Re-read of a test previously conducted on the IUS.


Final


Findings

Table A-2 provides the results of the WES analysis for calendar years 2015 through 2018.

Table A-2. Calendar Years 2015 to 2018 Medicaid FFS and MCO Whole Exome Sequencing Summary Utilization and Costs, CPTs 81415, 81416, 81417

2015 2016 2017 2018

Unique Individuals Under Study 21 41 58 91

Total WES Tests Conducted 23 75 108 184

Total Paid for all Tests $72,191 $46,510 $78,995 $163,346

Paid as a Professional Claim $62,650 $45,600 $76,886 $159,295

Average Paid - Professional Claim $12,530 $5,700 $4,055 $888

Paid as an Outpatient Claim $9,541 $910 $2,109 $4,051

Average Paid - Outpatient Claim $20 $16 $25 $32

Appendix A-Figure 1 depicts the age category distribution of members who received WES

testing.

Appendix A-Figure 1

26

56

80

28

9 12

0

20

40

60

80

100

<1 1 - 4 y.o. 5 - 12 y.o. 13 - 18 y.o. 19 - 23 y.o. > 23

Mem

ber

Co

un

t

Age Cohort

CY 2015 - 2018Medicaid FFS and MCO

Age Category Distribution of Members with a 81415 WES TestN = 210


Final


Appendix A-Figure 2 depicts the age distribution of tests by year.

Appendix A-Figure 2

Figure A-3 depicts the distribution of WES testing by CPT code and year.

Appendix A Figure A-3.

Age Cohort

0 20 40 60 80 100

2015

2016

2017

2018

Count

Ye

ar

CY 2015 - 2018

Medicaid FFS and MCO Utilization Whole Exome Sequencing CPT 81415 (N=210) & 81416 (N=179)

81416

81415


Final

Whole exome sequencing: final evidence report Page C-1

Appendix B. Search Strategy

PubMed Search, 2010 Through March 14, 2019

Search Query Items found

#1 Search (“Whole Exome Sequencing”[Mesh] OR “Whole Genome Sequencing”[Mesh] OR "whole exome"[All Fields] OR "whole exome"[All Fields] OR "whole genome"[All Fields] OR "whole-genome"[All Fields])

41129

#2 Search (“Cost-Benefit Analysis”[Mesh] OR “Genetic Diseases, Inborn”[Mesh] OR "Insurance, Health, Reimbursement"[Mesh] OR "Outcome Assessment (Health Care)"[Mesh] OR "Patient Care Management"[Mesh] OR “Precision Medicine”[Mesh] OR "Prospective Payment System"[Mesh] OR “Reproducibility of Results”[Mesh] OR “Sensitivity and Specificity”[Mesh] OR “diagnostic utility”[tiab] OR “Mendelian diagnostics”[tiab])

2997695

#3 Search (#1 and #2) 4248

#4 Search (#1 and #2) Filters: English 4163

#5 Search (#1 and #2) Filters: Publication date from 2010/01/01 to 2019/12/31; English 3610

#6 Search (#5 NOT ("Bacteria/genetics"[Mesh] OR “DNA, Plant”[Mesh] OR “DNA, Bacterial”[Mesh] OR "Fungi"[Mesh] OR "Genetic Predisposition to Disease"[Mesh] OR “Genome, Bacterial”[Mesh] OR "HIV"[Mesh] OR "Infection"[Mesh] OR "Neoplasms"[Mesh] OR Pregnancy[Mesh] "Viruses"[Mesh] OR "Virology"[Mesh] OR “bacterial DNA”[tiab] OR “bacterial typing”[tiab] OR “bacterial genetics”[tiab] OR cancer*[tiab] OR carcinoma*[tiab] OR “CRISPR-Cas”[All Fields] OR fungal[tiab] OR “gene editing”[tiab] OR HIV*[tiab] OR infection*[tiab] or infectious[tiab] OR neoplasm*[tiab] OR “plant DNA”[All Fields] OR pregnancy[tiab] OR pregnant[tiab] OR sarcoma*[tiab] or viral[tiab] OR virus*[tiab]))

2771

#7 Search ("Systematic Review" [Publication Type] OR “systematic review”[ti] OR “meta-analysis”[pt] OR “meta-analysis”[ti] OR “systematic literature review”[ti] OR “this systematic review”[tw] OR (“systematic review”[tiab] AND review[pt]) OR meta synthesis[ti] OR “cochrane database syst rev”[ta] OR "Umbrella Review"[tiab] OR "meta-analysis"[tiab] OR "meta-analyses"[tiab] OR "meta-synthesis"[tiab] OR "meta-syntheses"[tiab])

235171

#8 Search (#6 and #7) 19

#9 Search (#6 NOT (("Animals"[Mesh] NOT "Humans"[Mesh]) OR "Comment"[Publication Type] OR "Editorial"[Publication Type] OR "Case Reports"[Publication Type] OR Review[Publication Type]))

1699

#10 Search (“Whole Exome Sequencing”[All Fields] OR “Whole Genome Sequencing”[All Fields] OR “whole exome”[tiab] OR “whole exome”[tiab] OR “whole-genome”[tiab] OR “whole genome”[tiab] OR WES[tiab] OR WGS[tiab] or rWGS[tiab])

41960

#11 Search (“clinical benefit”[tiab] OR “clinical utility”[tiab] OR ClinSeq[tiab] OR “Cost-Benefit”[tiab] OR “cost effectiveness”[tiab] OR costs[ti] OR “diagnostic”[tiab] OR “disease management”[tiab] OR (health*[tiab] AND outcome*[tiab]) OR “inborn genetic diseases”[tiab] OR hospitalization*[tiab] OR (insurance*[tiab] AND reimburse*[tiab]) OR “medical management”[tiab] OR “Mendelian diagnostics”[tiab] OR “monogenic disease risk”[tiab] OR MDR[tiab] OR "Patient Care Management"[tw] OR “Precision Medicine”[tw] OR "Prospective Payment System"[tw] OR reimburse*[ti] OR “Reproducibility of Results”[tw] OR “Sensitivity and Specificity”[tw])

1750571

#12 Search (#10 and #11) 4372

#13 Search (“bacterial DNA”[tiab] OR “bacterial typing”[tiab] OR “bacterial genetics”[tiab] OR cancer*[tiab] OR carcinoma*[tiab] OR “CRISPR-Cas”[All Fields] OR fungal[tiab] OR “gene editing”[tiab] OR HIV*[tiab] OR infection*[tiab] or infectious[tiab] OR neoplasm*[tiab] OR “plant DNA”[All Fields] OR pregnancy[tiab] OR pregnant[tiab] OR sarcoma*[tiab] OR viral[tiab] OR virus*[tiab])

4516768

#14 Search (#12 not #13) 2948

#15 Search (#14 and ("2010/01/01"[edat]:"2019/12/31"[edat])) 2552

#16 Search (#15 and (#7 or "systematic review"[tiab])) 38

https://www.ncbi.nlm.nih.gov/pubmed

https://www.ncbi.nlm.nih.gov/pubmed/?cmd=HistorySearch&querykey=1
































Final


Search Query Items found

#17 Search (#16 not (#8 or #9)) 25

#18 Search (#15 not (#8 or #9 or #17)) 1812

PubMed Yield: 3,541

Embase Search, 2010 to March 14, 2019

Search Search History Results

#1 ‘Whole Exome Sequencing’/exp OR 'whole genome sequencing'/exp OR "whole exome" OR "whole exome" OR "whole genome" OR "whole-genome"

64,618

#2 'cost-benefit analysis'/exp OR 'genetic disorder'/exp OR 'reimbursement'/exp OR 'outcome assessment'/exp OR 'patient care'/exp OR 'personalized medicine'/exp OR “precision medicine” OR 'prospective payment'/exp OR 'reproducibility'/exp OR 'sensitivity and specificity'/exp OR 'diagnostic value'/exp OR “diagnostic utility” OR “Mendelian diagnostics”

3,099,850

#3 #1 and #2 14,235

#4 #3 and [English]/lim 14,062

#5 #4 and [2010-2019]/py 13,099

#6 #5 not ('bacteria genetics'/exp OR 'plant DNA'/exp OR 'bacterial DNA'/exp OR 'fungus'/exp OR 'genetic predisposition'/exp OR 'bacterial genome'/exp OR 'Human immunodeficiency virus'/exp OR 'infection'/exp OR 'neoplasm'/exp OR 'pregnancy'/exp OR 'virus'/exp OR 'virology'/exp OR ‘bacterial DNA’:ab,ti OR ‘bacterial typing’:ab,ti OR ‘bacterial genetics’:ab,ti OR cancer*:ab,ti OR carcinoma*:ab,ti OR ‘CRISPR-Cas’ OR fungal:ab,ti OR ‘gene editing’:ab,ti OR HIV*:ab,ti OR infection*:ab,ti or infectious:ab,ti OR neoplasm*:ab,ti OR ‘plant DNA’ OR pregnancy:ab,ti OR pregnant:ab,ti OR sarcoma*:ab,ti or viral:ab,ti OR virus*:ab,ti)

6,650

#7 'systematic review'/exp OR 'systematic review (topic)'/exp OR 'meta-analysis'/exp OR 'meta-analysis (topic)'/exp OR 'meta-analysis'/exp OR ‘systematic literature review’:ti OR ‘this systematic review’ OR (‘systematic review’:ti,ab AND 'review'/exp) OR ‘meta synthesis’:ti OR ‘cochrane database syst rev’ OR ‘Umbrella Review’:ti,ab OR ‘meta-analysis’:ti,ab OR ‘meta-analyses’:ti,ab OR ‘meta-synthesis’:ti,ab OR ‘meta-syntheses’:ti,ab

385,491

#8 #6 and #7 88

#9 #6 not (('animal'/exp NOT 'human'/exp) OR 'editorial'/exp OR 'case report'/exp OR 'review'/exp) 4,199

#10 ‘Whole Exome Sequencing’ OR ‘Whole Genome Sequencing’ OR ‘whole exome’:ti,ab OR ‘whole exome’:ti,ab OR ‘whole-genome’:ti,ab OR ‘whole genome’:ti,ab OR WES:ti,ab OR WGS:ti,ab or rWGS:ti,ab

65,278

#11 ‘clinical benefit’:ti,ab OR ‘clinical utility’:ti,ab OR ClinSeq:ti,ab OR ‘Cost-Benefit’:ti,ab OR ‘cost effectiveness’:ti,ab OR costs:ti OR ‘diagnostic’:ti,ab OR ‘disease management’:ti,ab OR (health*:ti,ab AND outcome*:ti,ab) OR ‘inborn genetic diseases’:ti,ab OR hospitalization*:ti,ab OR (insurance*:ti,ab AND reimburse*:ti,ab) OR ‘medical management’:ti,ab OR ‘Mendelian diagnostics’:ti,ab OR ‘monogenic disease risk’:ti,ab OR MDR:ti,ab OR ‘Patient Care Management’ OR ‘Precision Medicine’:ti,ab OR ‘Prospective Payment System’ OR reimburse*:ti OR ‘Reproducibility of Results’ OR ‘Sensitivity and Specificity’

2,005,600

#12 #10 and #11 6,892

#13 ‘bacterial DNA’:ti,ab OR ‘bacterial typing’:ti,ab OR ‘bacterial genetics’:ti,ab OR cancer*:ti,ab OR carcinoma*:ti,ab OR ‘CRISPR-Cas’ OR fungal:ti,ab OR ‘gene editing’:ti,ab OR HIV*:ti,ab OR infection*:ti,ab or infectious:ti,ab OR neoplasm*:ti,ab OR ‘plant DNA’ OR pregnancy:ti,ab OR pregnant:ti,ab OR sarcoma*:ti,ab OR viral:ti,ab OR virus*:ti,ab

5,740,507

#14 #12 not #13 4,171

#15 #14 and (‘2010/01/01’[edat]:’2019/12/31’[edat]) 3,940

#16 #15 and (#7 or ‘systematic review’:ti,ab) 92






















Final


Search Search History Results

#17 (#16 not (#8 or #9) 55

#18 #15 not (#8 or #9 or #17) 2,771

#19 #8 or #17 143

Embase Yield: 2,914 (1,610 after deduplication)

Cochrane Library Search, 2010 to March 14, 2019

ID Search Hits

#1 "whole exome":ti,ab,kw OR "whole exome":ti,ab,kw OR "whole genome":ti,ab,kw OR "whole-genome":ti,ab,kw OR rWGS:ti,ab,kw OR WES:ti,ab,kw OR WGS:ti,ab,kw

621

#2 “bacterial DNA”:ti,ab,kw OR “bacterial typing”:ti,ab,kw OR “bacterial genetics”:ti,ab,kw OR cancer*:ti,ab,kw OR carcinoma*:ti,ab,kw OR “CRISPR-Cas”:ti,ab,kw OR fungal:ti,ab,kw OR “gene editing”:ti,ab,kw OR HIV*:ti,ab,kw OR infection*:ti,ab,kw or infectious:ti,ab,kw OR neoplasm*:ti,ab,kw OR “plant DNA”:ti,ab,kw OR pregnancy:ti,ab,kw OR pregnant:ti,ab,kw OR sarcoma*:ti,ab,kw OR viral:ti,ab,kw OR virus*:ti,ab,kw

332111

#3 #1 not #2 293

#4 #3 with Cochrane Library publication date Between Jan 2010 and Dec 2019 285

Cochrane Yield: 285 (236 after deduplication)

Total Bibliographic Database Yield: 5,387

ClinicalTrials.Gov Search, 2010 to April 9, 2019

( "whole exome" OR "whole exome" OR "whole genome" OR "whole-genome" OR rWGS OR

WES OR WGS ) AND NOT ( "bacterial DNA" OR "bacterial typing" OR "bacterial genetics"

OR cancer* OR carcinoma* OR "CRISPR-Cas" OR drug* OR fungal OR "gene editing" OR

glioma* OR healthy OR HIV* OR infection* or infectious OR leukemia* OR neoplasm* OR

"plant DNA" OR predisposition* OR pregnancy OR pregnant OR radiation* OR sarcoma* OR

tumor* OR viral OR virus* ) AND INFLECT ( "01/01/2010" : "04/09/2019" ) [LAST-UPDATE-

POSTED]

CT.gov Yield: 145 (after deduplication)

Other Data

We searched websites of the organizations listed in Table B-1 to identify related health

technology assessment, clinical practice guidelines, position or policy statements, payor

coverage policies, or other clinical guidance.




Final


Appendix B-Table 1. Websites Searched for Documents Relevant to Whole Exome Sequencing

Organization Potentially Relevant

Documents

American Academy of Pediatrics 0

American Academy of Neurology 1

Society for Developmental and Behavioral Pediatrics 0

American College of Medical Genetics and Genomics 5

National Institute for Clinical Excellence 0

Institute for Clinical and Economic Review 0

University of York Centre for Reviews and Dissemination/National Institutes for Health Research 4

U.S. Food and Drug Administration 0

U.S. Agency for Healthcare Research and Quality 0

Centers for Medicare and Medicaid Services 0

Aetna 1

Cigna 1

Humana 1

BlueCross BlueShield (Premera and Regence) 1

Kaiser Permanente 1

United Health 1

Tricare 0

Abbreviations: U.S. = United States


Final


Appendix C. Evidence Tables

Table C-1. Characteristics of Included Studies ..................................................................... C-1

Table C-2. Clinical Utility Outcomes ................................................................................. C-37

Table C-3. Health Outcomes ............................................................................................... C-43

Table C-4. Safety Outcomes ............................................................................................... C-44

Table C-5. Characteristics of Included Studies Reporting Cost Outcomes ........................ C-48

Table C-6. Findings from Studies Reporting Cost Outcomes ............................................ C-52


Final


Table C-1. Characteristics of Included Studies

Author (Year) Study Design Country Year (s) Conducted Study Funder

Number of participants Study setting and population

Description of test or testing strategy; comparator strategies evaluated (if applicable); and reported diagnostic yield

Balridge (2017)43

Single-arm observational cohort

U.S. 2012-2015 NIH, Washington University

155 155 patients seen at Washington University Exome clinic; phenotypes were 66% (103) neurological, 10% (15) multiple congenital anomalies, 10% (15) mixed and 14% (21) another phenotype; mean age 6 years (range: 3 days to 33 years); 44% (68) female, 84%; (130) White

Trio WES (n=128), parent, sibling proband WES (n=1), parent/ sibling WES (n=6), or singleton WES (n=20) performed at three commercial laboratories Variants initially classified by the laboratory, then reassessed and reclassified by clinical geneticist. Diagnostic yield: 67 (43%) definitive diagnosis

Bourchany (2017)42


France NR Regional Council of Burgundy

29 patients enrolled, 23 pediatric patients, 4 fetuses after pregnancy termination, and 2 adults

29 unrelated patients at five French genetics centers. Patients had expert consultation with clinical geneticist from a reference center for congenital anomalies. Inclusion criteria were association of undiagnosed developmental disorder and ongoing pregnancy of at-risk relatives requesting genetic counseling; hospitalization in an intensive care unit with a diagnostic request for guiding care Female = 48.3%* (14/29) Ethnicity = NR Mean age = 5.8 years (range: 0 months to 37 years)

Trio WES, variants annotated with SeattleSeq SNP Annotation 138; looks for MAF <0.01 in dbSNP and ExAC, filtered based on local database of 69 healthy individuals using MAF >0.05%, OMIM genes, phenotypic concordance considered Sanger sequencing to confirm, 21/29 patients had multiple genetic and metabolic tests before WES All patients had array CGH before or during inclusion. WES was first-line test in 8/29. Diagnostic yield: 13/29 = 44.8% For non-fetuses, diagnostic yield = 44%*(11/25)

Cordoba (2018)18


Argentina NR National Research Council Argentina

40 Consecutive series of 40 patients (adults and children) suspected of neurogenetic conditions from a neurogenetics clinic in tertiary hospital, mean age: 23 years (range: 3 to 70 years) Phenotypes included myopathy, ataxia, encephalopathy,

Singleton or trio WES, variants confirmed by Sanger sequencing Classification of variants based on ACMG and Association for Molecular Pathology; variants classified as positive, negative, or undetermined Diagnostic yield: 40% (this includes 2 cases that were reclassified from undetermined and negative category at the time of their original report after subsequent reanalysis)


Final





developmental delay, dystonia, among others

Daga (2018)41 Single-arm observational cohort

U.S. 2013-2016 NIH, National Research Fund of Korea, Deutsche Forschungsgemeinschaft

65 individuals, 51 families

Patients who presented at children's hospital with nephrolithiasis and/or a finding of nephrocalcinosis on renal ultrasound, before the age of 25 years and their families, study families selected for DNA available for multiple affected family members (n=49), recurrent or early-onset disease (n=7), or both parents available for trio analysis (n=15)

WES of multiple family members or trios Whole exome analysis Diagnostic yield 15 of 51 (29.4%) No comparator pathways

Dillon (2018)17 Single-arm observational cohort

Australia 2016 Melbourne Genomics Health Alliance, State Government of Victoria; Bioplatforms Australia

145 children A retrospective simulation study of panel testing in 145 children who had undergone WES for diagnostic purposes; age range: 0-12 months = 46% (67); 12-24 months= 19% (28); >24 months= 35% (50) Includes participants suspected of having a genetically heterogenous condition or features. Female = 43% (63). Primary phenotype of patients consisted of: Dermatological 3% (4), dysmorphic with congenital abnormalities 45% (65), Neurometabolic 30% (43), skeletal dysplasia 9% (13); ophthalmological 3% (4), other 11% (16). Additionally, 43% (62) had an intellectual disability and 57% (83) did not. During recruitment, clinicians were required to propose commercial gene panel tests as an alternative to WES and nominate a phenotype-driven candidate gene list

WES with variants prioritized based on a phenotype-driven list. Only variants in genes known to cause human disease (the "Mendeliome") were analyzed. Variant classification was performed per the ACMG standards. Diagnostic yield with WES was 78/145 (53.8%). Comparator testing strategies were simulated by applying up to three commercial panels to each of the children who were diagnosed with WES. The three panels were chosen based on those most likely to sufficiently cover the differential diagnosis provided at recruitment

Ding (2014)75 Modeling Study Australia NR NR 24 autosomal dominant conditions -

24 autosomal dominant conditions were selected because they were highly penetrant, asymptomatic for

No sequencing was performed during the course of this study. This is exclusively a mathematical modeling paper


Final





including one semidominant condition

long periods of time, and amenable to preventive measures and/or treatment. A simple mathematical model was developed based on binomial distribution which represents the probability of reporting at least 1 incidental finding. A diagnostic panel was constructed based on the 24 ACMG-recommended minimum list of genes to be reported

Dragojlovic (2018)23

Modeling Study Canada 2016 British Columbia Children’s Hospital Foundation, Canadian Institutes of Health Research

167 families were sequenced, but final outcomes not completed in 2016, the year of the analysis so outcomes unknown.

A cost modeling study for the Clinical Assessment of the Utility of Sequencing and Evaluation as a Service study to test a delivery model for diagnostic exome sequencing in pediatric patients. No further information on study population provided

Evaluated both singleton and trio WES, in separate scenarios; targeted analysis focused on known disease genes; variants classified as definite or probable were confirmed with Sanger sequencing; reanalysis of negative results every 6 or 12 months until end of study. Diagnostic yield Trio WES after genomics consultation: 34.3% (95% CI, 23.2 to 46.5) Singleton WES: 28.1% (95% CI, 12.9 to 42.9) Trio WES without clinical genomics consultation first: 34.0% (95%CI, NR)

Evers (2017)52 Single-arm observational cohort

Germany 2013 - 2015 NR 72 patients from 60 families

WES was performed in a cohort of 72 patients from 60 families with undiagnosed, suspected genetic conditions. Patients were phenotypically characterized prior to WES. The cohort was comprised of 45 index patients with developmental delay and/or congenital malformations, eight patients with infantile dystonia, and seven patients with a neurometabolic disorder. All patients

Trio-based (a few cases include affected or unaffected siblings), WES (allowed for novel variant discovery), excluded variants with MAF >1% in ExAC or 1KGP3 and local controls to exclude other common alleles/technical artifacts. Annotated using ANNOVAR. Assessed by 7 variant effect prediction tools (e.g., SIFT). ACMG guidelines used to classify, additional criteria for variants in genes not previously associated with phenotype.


Final





were evaluated by an experienced clinical geneticist and/or neuro-pediatrician. The patient had a mean age at diagnosis of 8.5 years compared to a mean age at WES analysis of 6.4. 50% of patients were female, most were of German and Turkish origin (55% and 28%, respectively), 25% of families reported consanguinity (mainly those of Turkish descent), 70% of index patients with known family history were sporadic cases, 30% had at least one affected sibling. Patients displayed a wide range of symptoms, with 77% having developmental delay/intellectual delay; other common phenotypes were micro or macrocephaly (53% and 12%, respectively), dysmorphic signs (40%), short stature (32%), epilepsy (28%), and behavioral abnormalities including autism spectrum disorders (18%). 32% had congenital malformations, most often congenital heart disease. Characteristics of the congenital malformations group of 45 patients were: Female = 23 (51%); Age at molecular diagnosis < 10 years = 11 (69%); ≥ 10 years = 5 (31%); Age at WES < 10 years = 14 (61%); ≥10 years = 9 (39%)

Diagnostic yield: 21/60 families (35%) overall 16/45 (36%) neurodevelopmental disorders 3/7 (43%) neuromuscular disorders 2/8 (25%) dystonias Comparator: NA


Final





Ewans (2018)21 Single-arm observational cohort

Australia 2013-2014 Australian National Health and Medical Research Council

54 patients from 37 families

Adults and children recruited from clinical genetics units with distinctive phenotype likely to have a monogenic etiology, a family structure consistent with Mendelian inheritance, and prior diagnostic investigations that were all negative. Phenotypes included syndromic intellectual disability (49%), skeletal (13%), hematological (11%), nonsyndromic intellectual disability (8%), visual (8%), neurological (5%), metabolic (3%) and other syndromal disorder (3%) Age: 68% children Sex: NR

Mixed approach of singleton, trio, including multiple affected family members; whole exome analysis with variants prioritized by pedigree structure (tested all possible inheritance patterns); used ACMG pathogenicity criteria and required adequate relationship of variant with published gene-disease evidence, Sanger validation. 12-month reanalysis: undertaken for undiagnosed families after initial testing Diagnostic yield for initial WES: 11/37 families (30%) 46% of trios had molecular diagnosis from WES 22% of singletons had molecular diagnosis from WES 20% of multiple affected individual families had molecular diagnosis from WES. Diagnostic yield for WES reanalysis: 4 additional families diagnosed. Overall diagnostic rate from 30% to 41% Counterfactual comparison of WES to "traditional diagnostic pathway" 14 patients with intellectual disability and available medical records used for comparison 1) WES available at initial clinical genetics service contact 2) WES available at initial presentation "with clinical symptoms that would warrant genetic testing" 3) traditional pathway

Hauer (2017)51 Single-arm observational cohort

Germany NR Centre for Clinical Research of the University of Erlangen-Nurnberg

565 enrolled, 200 exome analysis patients

565 patients were systematically phenotyped, patients referred by local medical specialists for evaluation of growth retardation/ short stature, 551 were of European descent, 13 of Asian and 1 of Arab

Trio analysis In 100 probands and singleton in another 100 probands Targeted WES (1000 known short stature genes from MedGen and Human Phenotype ontology) Variant reporting: variants assessed


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descent. At the time of enrollment, 83% were under the age of 18 years: < 4y = 102 (18%); > 4y =463 (82%); female = 349 (62%); male = 216 (38%) 81% presented with a height of 2 SDs below the age related mean, remaining 19% were 2 SDs below the estimated family target height. Overall 20% showed mild learning disabilities and 21% microcephaly, 30% underwent bone age evaluation and of those 84% had either delayed or accelerated bone ages. All 565 underwent extensive prior endocrinological and diagnostic workup to exclude defects of the growth hormone pathway and organic causes of their growth deficit. A representative group of 200 patients selected from the 565 patients and their families, where unbiased exome sequencing was performed. These patients had short stature of unknown origin Patients were ages: < 4 years = 33 (17%); > 4 years = 167 (83%); female = 122 (61%) and male = 78 (39%)

according to inheritance pattern using in-house tool. Only variants called with GATKHap, GATKUG, or SNVer were analyzed, had at least 10% of average coverage of patient's exome, and for at least 5 novel alleles were detected. Excluded variants with MAF <= 0.001 in 1KG, Exome Variant Server, or ExAC, or <= 0.15% in in-house variant database, different cutoffs used for different zygosities Sanger sequencing done for confirmation and familial segregation, applied ACMG criteria for variant classification Diagnostic yield: 33/200 (16.5%) No comparator pathway

Howell (2018)28 Other Australia 2011-2015 NR 114 evaluated, but of these only 49 had unknown etiology/diagnosis, and only some of these

Population based study of 86 infants with severe epilepsies of infancy. Infants were born in Victoria, Australia during 2011 - 2015 and identified through EEG laboratories, NICU databases, and neurologist referrals. Severe epilepsies of

All participants received one or more of the following testing pathways. "Research genetic testing" defined as targeted WES (n=40), molecular inversion probes with panels of 39 to 65 epilepsy genes (n=32), single-gene sequencing


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received WES testing.

infancy was defined as age <18 months with 1) frequent seizures ( >/= daily for 1 week or >/= weekly for 1 month), 2) ongoing seizures despite trials of 2 appropriate antiepileptic drugs, and 3) epileptiform EEG abnormality. Infantile spasms were automatically included. Mean age: NR; % Female: NR; Race/ethnicity: NR

(n=1), and whole genome sequencing (n=1), singleton WES with targeted analysis of 341 genes (n= NR). Tier 1 testing defined as brain MRI, CMA, blood count, electrolytes, urea, creatinine, glucose, calcium, magnesium, phosphate, liver function tests, lactate, ammonia, amino acids, acylcarnitines, biotinidase, uric acid, urine tests for organic and amino acids, piperideine-6-carboxylate, S-sulfocysteine, guianidioacetic acid, purines, pyriminidines Tier 2 testing defined as common mitochondrial mutations, polymerase gamma common mutations, transferrin isoforms, copper, ceruloplasmin, very long chain fatty acids, white cell enzymes, paired blood-CSF evaluation, CSF neurotransmitters Tier 3 testing defined as skin biopsy, electron microscopy for changes of neuronal ceroid lipofuscinosis, lysosomal and mitochondrial disorders, fibroblast culture, liver and muscle biopsies for histopathology, histochemistry, electron microscopy, respiratory chain enzyme analysis Diagnostic Yield By Pathway Path 1 (Tier 1, Tier 2, Repeat MRI, Tier 3) ->39/86= 45.3% Path 2 (Tier 1, Tier 2, Repeat MRI, Tier 3, WES)->48/86=55.8% Path 3 (Tier 1, Tier 2, Repeat MRI, WES, Tier 3)->48/86= 55.8% Path 4 (Tier 1, Tier 2, WES, Repeat MRI, Tier 3)->48/86= 55.8%


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Path 5 (Tier 1, WES, Tier 2, Repeat MRI, Tier 3)->48=55.8% Path 6 (Tier 1, WES, Repeat MRI, Tier 2)->48/86=55.8% Path 7 (Tier 1, WES, Repeat MRI)->46/86=53.5%

Iglesias (2014)46


U.S. 2011-2013 NR 115 Retrospective chart review of 115 patients whose WES results were clinically evaluated at an academic health care center with a variety of phenotypes, including 25.2% (29) developmental delay/intellectual disability, 24.3% (28) birth defects, and 12.1% (14) seizures, 78.9% (94) children, 3 (2.6%) were terminated fetuses, 48.6% (56) female, 61.7% (71) white

95/115 (92.6%) had parent and proband trio submitted 3/115 (2.6%) proband only not explicit, but discover novel candidate genes, so suspect whole exome analysis Clinical interpretation of WES done by ordering geneticist Diagnostic yield = 37/115 (32.2%) 15/28 (53.5%) for birth defects 10/29 (34.4%) for developmental delay/ intellectual disability 3/7 (42.9%*) for cardiomyopathy 3/4 (75%*) for ophthalmologic disease 2/4 (50%*) myopathies 2/4 (50%*) dermatologic disease 2/2 (100%*) neurological/neurodegenerative disorders 1/2 (50%*) metabolic disorder No comparator testing pathway.

Jones (2018)50 Qualitative research design

U.S. 2015-2016 Regeneron Genetics Center, Geisinger

28 participants assessed; 23 included

A retrospective chart review was performed to monitor disease manifestation and medication management after learning Familial hypercholesterolemia (FH) genetic results. The 28 individuals were invited to participate in semi-structured interviews to understand their experience learning these results. Only individuals with a Geisinger primary care provider were included in the chart review portion of this study (N=23) due to

Singleton Not explicitly stated targeted or whole exome WES procedures NR (MyCode program) Diagnostic yield: NA Comparator testing: NA


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availability of documentation about disease management and control within the electronic health record. Female = 15 (65%), median age = 66 years (range: 27 to 85). Conditions included high prevalence of hyperlipidemia 19 (83%), coronary atherosclerosis 11 (48%), and peripheral vascular disease 8 (35%). Thirteen (57%) had documented FH on their problem list after receipt of the test result, whereas only 5 (22%) had a diagnosis on their problem list before receipt of their result. Seven (30%) had a history of myocardial infarction or cerebrovascular disease. Nearly half had a diagnosis of hypertension n = 11 (48%) and 8 (35%) had a prior history of smoking, 2 (8%) were active smokers

Jurgens (2015)65


U.S. NR NIH 232 individuals from 89 families analyzed

232 individuals from 89 families sequenced Underwent WES for variety of potential Mendelian disorders Sequenced at Johns Hopkins University, a tertiary academic health care center/research university 73% self-identified as European descent

At least some were family-based WES Targeted for this report (only 56 genes in ACMG guidelines analyzed) Other genes probably analyzed for diagnosis Web-based system PhenoDB. Variant classification using HGMD, ClinVar, and Emory Genetics Laboratory Variant Classification Catalog. Classification of variants within these databases was often discordant (~45.8% shared variants discordant). To determine what variants are reportable, followed criteria from Dorschner et al. Reported pathogenic and likely


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pathogenic variants to participants. Diagnostic yield: NR

Lee (2015)73 Single-arm observational cohort

U.S. 2012-2013 NIH 29 eligible, 26 (approx. 90%) enrolled

Patients with presumed hereditary retinal dystrophies were enrolled during clinical appointments in a university-based ophthalmic genetics clinic during initial or follow-up visits. Return patients were eligible if the molecular etiology of their retinal disorder was unknown 5 of 26 participants (19%) were under 18 years of age; adults ranged from 22 to 69 years. 3 of 26 participants (11%) had an uncertain clinical diagnosis at time of enrollment. 14 of 26 participants (54%) did not have a known family history of retinal dystrophy

Exome sequencing used Agilent’s SureSelect XT Target Enrichment System for Illumina paired-end sequencing on the HiSeq 2000 instrument. Average coverage depth across the entire region targeted for enrichment was 58.19. Custom pipeline developed for the NCGENES project used to process raw sequence data from FASTQ files to generate variant calls. Filtered variants using a list of 186 genes associated with syndromic and nonsyndromic retinopathies. Variants where then prioritized to select ones previously reported as pathogenic, truncating or missense variants with MAF <1%, and other categories. Diagnostic yield: 15 of 26 participants (58%) No comparator testing

Li (2019)67 Qualitative research design

U.S. 2015-NR NR 38 families eligible, 15 consented, 14 analyzed (including 1 husband-wife dyad)

14 telephone interviews with 15 parents or legal guardians (1 interview with both parents) who received WES results for their children in the past 6 months that included variant(s) of uncertain significance at a large academic hospital, various phenotypes including macrocephaly, microcephaly, encephalopathy, failure to thrive, developmental delay, intellectual disability, learning disabilities, language disorder, epilepsy, gait abnormality, hemiparesis, autism spectrum disorder, attention deficit

hyperactivity disorder, self‐injurious behavior, congenital hip dysplasia,

Type of WES not specified Targeted vs whole exome analysis not specified Participants were restricted to those with WES completed after May 2015 due to timing of ACMG guidelines that were the focus of this paper. Diagnostic yield: NA Comparator testing pathways: NA, but no patient had clinical genetic diagnosis prior to WES


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and heart defect, mean parent age: 45 years (range: 29 to 62), 12 female, 7 White, 8 with post-graduate degrees, mean child age: 7.5 years (range: 1.5 years to 15 years)

Mann (2019)48 Single-arm observational cohort

U.S. Patient renal transplants - 2007 to 2017 WES completed after transplant, implies 2018

NIH, Yale Center for Mendelian Genomics

272 probands met inclusion criteria, 41 excluded for care at different hospital, 18 excluded for inability to provide consent, 2 excluded for death, 23 excluded for secondary renal disease instead of primary, 45 declined, and 39 not contacted 104 enrolled and analyzed

Patients with chronic kidney disease that developed disease before 25 years of age and were transplanted between 2007 and 2017 at Boston Children's Hospital 55/104 (52.9%) diagnosed with congenital anomalies of the kidney and urinary tract, 21/104 (20.2%) with steroid-refractory nephrotic syndrome, 7/107 (6.8%) with chronic glomerulonephritis, 9/104 (8.6%) with renal cystic ciliopathy, and 3/104 (2.9%) with nephrolithiasis, 9/104 (8.6%) cause of renal disease unknown male = 62/104 (59.6%) race/ethnicity = NR age = NR 9/104 (8.7%) from consanguineous families

Singleton (not explicitly stated) Targeted exome (396 CKD genes) variant filtering using population databases (Exome Sequencing Project, ExAC, gnomAD, 1KG) to include only MAF < 0.01 except for NPHS2 R229Q allele. Excluded synonymous and intronic variants outside splice site regions. Six/396 genes did not achieve 30X coverage Ranked variants based on likelihood of causing disease using conservation metrics and pathogenicity prediction (PolyPhen2, SIFT, MutationTaster), then subjected to literature review, clinician/scientist review, and ACMG criteria to report pathogenic or likely pathogenic for molecular diagnosis. Sanger sequencing confirmed. WES not used to identify novel CNVs, but if SNP array showed pathogenic CNV and WES negative for SNV/indels, performed CNV analysis on WES data using CoNIFER diagnostic yield: 34/104 (32.7%) had monogenic cause of CKD. Among patients with history of consanguinity, diagnostic yield was 67%, extra-renal manifestations = 45%, and patients with a positive family history = 48% Comparator strategy: Not explicitly done, not compared systematically, 6 patients out of 34 with WES diagnostics had previously


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obtained molecular diagnosis from clinical genetic testing (6/34=17.6%*) and 5 families had molecular diagnosis from targeted gene sequencing (5/34=14.7%*) 23/34 (82.4%*) individuals who underwent WES received diagnosis for first time due to WES However, it was not stated how many of 104 underwent previous diagnostic odysseys in search of a molecular diagnosis and with what tests

Matias (2019)49 Controlled (two or more groups) observational cohort

U.S. 2013-2015 NR 102 eligible, 78 analyzed

Children referred for WES at a tertiary children's hospital who received either positive (n=37) or negative (n=41) results with phenotypes including 38% (30) neurologic, 24% (19) immune, and 19% (15) neurologic + multiple congenital anomalies/DF, mean age: 7.0 years (range 2.8 to 14.3 years), 55% (43) female, 87% (67) White

Singleton = 3/78 (4%), doubleton = 3/78 (4%), trio = 61/78 (79%), and more than trio = 10/78 (13%) Not explicitly stated targeted vs whole exome analysis NR on method reporting Diagnostic yield = 37/78 (47.4%*) No comparator analysis

McConkie-Rosell (2018)69


U.S. NR NIH 65 parents of 39 affected children probands were offered the study; 50 parents of 31 probands were enrolled

Data for the study were collected prospectively at the clinical site. Parents completed study measures on the first day of the Undiagnosed Diseases Network evaluation. Female = 60% (30), ages of 50 parents: male ages 25 - 39 = 9; 40-54 =11, female ages 25-39 = 17; 40-54 = 13, race/Ethnicity of parents (self-reported), Caucasian = 86%, age of the 31 children: mean +/- SD and minimum-maximum in years): 7.83 +/- 4.96 (1-18)

NR


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Meng (2017)40 Single-arm observational cohort

U.S. 2011-2017 March of Dimes, NIH

278 Infants less than 100 days old at time of testing at a large academic children's hospital referred for exome sequencing for a range of medical concerns, median (SEM) patient age at sample submission: 28.5 days (1.7), 45.7% female

Proband WES, n=176 (63%); Trio WES, n=39 (14%), Critical Trio WES, n=63 (22%) Whole exome analysis Variants interpreted according to ACMG guidelines and Baylor Genetics guidelines Diagnostic yield, 102 of 278 (36.7%). Critical exome sequencing, 50.8% (p=0.01). Dual diagnoses, 3.9%. Diagnosis not recognized on initial examination: 4 of 102. No comparator testing pathways.

Monies (2017)61 Single-arm observational cohort

Saudi Arabia 2016 The laboratory (Medical Diagnostic Laboratory) is revenue-generating for KFSHRC.

First 1013 families referred to NGS diagnostics at this laboratory 347 families received WES in at least proband, including 15 families after negative NGS panel

Sole major referral NGS laboratory in Saudi Arabia Families referred to reference lab for NGS-based assays in Saudi Arabia for tests including multigene panels and WES. No particular selection criteria applied. 7 "clinically-themed" multigene panels offered, others got WES. WES n=347 families: 321 Solo (proband) only tested, parents included in trio tests (n=17). Couples with history of prior affected children offered duo tests if no affected children available for testing. Duo testing also requested in some cases of two affected siblings (total duo = 9). However, duo testing may also include parents who have lost children. Solo WES = 321/347 (92.5%*) Trio WES = 17/347 (4.9%*) Duo WES = 9/347 (2.6%*) Female = 150/347 (43.2%*)

92.5%* had singleton WES, 4.9%* had trio, 2.6%* had duo. Whole exome analysis performed Used variant calling/annotation pipeline based on BWA, Samtools, GATK, and ANNOVAR using public domain data from ANNOVAR and in-house databases for Saudi disease variants. Reported variants based on previously reported disease-causing variants relevant to patient's phenotype & checked for pathogenicity (likely causal). If none identified, searched for other variants, including evaluating novel variants for pathogenicity if loss-of-function; = likely causal. Missense and in-frame indels usually reported as VOUS If only one heterozygous candidate variant identified, reported as ambiguous If no candidate variants in no disease genes, considered variants in genes not previously linked to human diseases with suspicions, but considered ambiguous Positive results = "pathogenic or likely pathogenic variants in known disease genes that explain the phenotype in the correct zygosity" Variable Sanger validation


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Consanguinity = 136/347 (39.2%*) mean age = 8.9*, sd = 9.6*

Diagnostic yield: 43% of those tested using WES 666 families subjected to one of 7 offered panel testing with 27% positive result (referring physician decision for panel vs WES)

Monroe (2016)80


Netherlands 2011 The Wellcome Trust 17 17 patients seen in a tertiary specialty clinic that specializes in diagnosing children with intellectual disability. The patients met the following criteria: patient was born to healthy, unaffected parents; the parents were nonconsanguineous; both parents could be contacted and were able to give consent; and the patient was undiagnosed at the time of the study. Patients first visited the clinic at a median of 1.1 years and an average of 3.0 years., range from 0 to 11.8 years;% female = 59%

Trio WES, variants validated with Sanger Sequencing Diagnostic yield: 5 (29.4%) The comparator strategy included traditional diagnostic evaluation, which included labs, imaging, and other genetic tests other than WES

Muramatsu (2017)70


Japan 2011-2013 Ministry of Health, Labor and Welfare of Japan

371 IBMFS patients: 121 targeted sequencing, 250 (WES)

371 patients with Inherited Bone Marrow Failure Syndrome (IBMFS). Targeted sequencing: 121, WES: 250 WES patients included patients diagnosed with Fanconi Anemia (FA) (73); diamond blackfan anemia (61); Hemolytic Anemia (44); dyskeratosis congenita (29); congenital dyserythropoietic anemia (12); congenital sideroblastic anemia (9); congenital amegaryocytic thrombocytopenia (7); hereditary hemophagocytic lymphohistiocystosis (6); severe congenital neutropenia ( 3);

Singleton WES Whole exome analysis Variant allele frequency >0.20 used as cut-off value for variant detection. VAF >0.01 in ESP6500 or 1000 genomes considered common polymorphisms. Diagnostic yield of WES: 68 of 250 (27%) Diagnostic yield of targeted sequencing (184 genes): 53 of 121 (44%)


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unclassified/other (6) . Most of the patients 182 (73%) underwent various genetic tests with negative results before WES analysis

Niguidula (2018)39


U.S. 2015-2016 Ambry Genetics 62 health care providers who returned surveys regarding patients referred for WES

Survey of health care providers receiving patient WES report from commercial laboratory, 2,876 surveys sent and 2.2% (62) returned, patient phenotypes

included 33.9% (21) non‐specific and complex neurodevelopmental disorder, 8.1% (5) multiple congenital anomalies, 6.5% (4) cancer susceptibility, 4..8% (3) movement disorders, and 4.8% (3) cardiovascular symptoms, patients median age 5.5 years, 28 (45.2%) female

WES testing not described, findings in clinically characterized genes were classified in four categories: 1) positive, relevant alteration detected; 2) likely positive, relevant alteration detected; 3) uncertain, alterations of uncertain clinical relevance detected; 4) negative, no relevant alterations detected Of survey respondents, 37.1% were from providers that had reports with a positive or likely positive pathogenic alteration

Nolan (2016)24 Single-arm observational cohort

U.S. 2011-2015 no specific grant or funding source

135: WES recommended, 53: WES done, 50: Had WES results at time of analysis

Patients in an academic pediatric neurology clinic who were referred for diagnostic WES testing, 88% with neurodevelopmental delay, mean age: 7 years, 5 months, 46% female, 85% White

Singleton and/or Trio plus affected siblings WES with whole exome analysis. Analysis performed by two outside laboratories. Variants called as pathogenic or likely pathogenic were considered diagnostic. Diagnostic yield: 24 (48%)

Palmer (2018)27 Single-arm observational cohort

Australia NR SEALS Genetics Laboratory, Garvan Institute, Kinghorn Foundation, National Health and Medical Research Council

48 eligible; 32 enrolled, 30 analyzed

Children with infantile-onset epileptic encephalopathy, who remained undiagnosed after “first-tier” testing at a children's hospital, mean age: 46.6 months, 47% (15) female. Criteria for infantile-onset epileptic encephalopathy based on ILAE definition (Berg et al. 2010)

First-tier testing comprised pediatric neurology and clinical genetics consultation, brain MRI, routine EEG, urine, blood, and cerebrospinal fluid studies (basic biochemistry, blood gas, TORCH screen, urine metabolic screen, B12, folate, copper, ceruloplasmin, selenium, zinc, plasma amino acids, AASA and P6C, lactate, glucose,), CMA, and single-gene testing if a single monogenic condition was suggested by the patient's phenotype. Second-tier testing comprised multiple other


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blood, urine and CSF tests, screening for common expansions in ARX, common mitochondrial deletions and duplications, methylation, sequencing of specific genes, next-generation sequencing panel for EE, PET scan, MRS scan, CT scan, Tc99m radionucleotide scan, trio WES conducted using in-house platform. Studies compared standard diagnostic pathway (first and second tier testing without WES) to exome diagnostic pathway (first and second tier testing + trio WES +Sanger confirmation ). Diagnostic yield: Standard path: 6.3% (2 of 32 tested) Exome path: 53% (16 of 30 tested)

Perucca (2017)34


Australia 2014 National Health and Medical Research Council of Australia

42 eligible; 40 consented and enrolled

Consecutive patients >4 weeks old, diagnosis of focal epilepsy, no epileptogenic lesion on MRI, and ≥1 1st or 2nd degree relative with history of febrile seizures or any epilepsy type. Exclusions: Previous genetic testing except chromosomal microarray, severe intellectual disability, benign epilepsy with centro-temporal spikes, and benign occipital epilepsy. 24/40 (60%) diagnosed with temporal lobe epilepsy, 6/40 (15%) with frontal lobe epilepsy, 1/40 (2.5%) with parietal lobe epilepsy, and 1/40 (2.5%) with occipital lobe epilepsy. Undefined localization in 8/40 (20%) of patients

singleton Initial analysis of 27 focal epilepsy genes and 35 non-focal epilepsy genes, then included 2 additional focal epilepsy genes later discovered (1 likely pathogenic variant detected) Identified variants reviewed by expert panel, population databases, online tools (e.g., SIFT, PolyPhen). Used ACMG classifications as pathogenic or likely pathogenic. Validated by Sanger sequencing and family segregation when possible 5/40 (12.5%) had pathogenic or likely pathogenic; 1 detected after reanalysis NA comparator pathways


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female: 16/40 (40%) age: median 32.5, range 2-74 80% had single first or second degree affected relative

Posey (2015)63 Single-arm observational cohort

U.S. 2011-2014 NIH

4476 individuals with diagnostic singleton WES; 505 adults over age 18; 486 excluding related individuals and those referred without clinical indication. 272 included in previous reports

Unrelated adults with varied diagnosis referred for WES for clinical diagnosis age: 18-30, 255/486 (52.5%); >70 12/486 (2.5%) Mean/median age NR Females = 247/486 (50.8%*) Mixed European Caucasian descent = 71.7% African American = 3.6% Hispanic = 12.6% Mixed ethnicity = 6.0% Unknown ethnicity = 72/486* (14.8%) Parental consanguinity = 22/486 (4.5%)

Singleton WES Parental samples used for Sanger confirmation) Whole exome analysis (No specified conditions or genes, report of secondary findings) Coding SNP array for QC Mitochondrial sequencing Variant classification consistent with ACMG guidelines Diagnosis required pathogenic or likely pathogenic variant in Mendelian disease genes consistent with phenotype and inheritance pattern observed clinically. Diagnostic yield: 82*/486 (16.9%*). Excludes 3 diagnoses from mitochondrial sequencing. No comparator pathways

Ream (2014)58 Single-arm observational cohort

U.S. NR NR 37 new patients (19 boys) with DRE of which 25 underwent genetic testing. 4 established patients with WES. Total WES: 6, 2 new and 4 established.

New patients at tertiary care center pediatric epilepsy clinic diagnosed with pediatric drug resistance epilepsy in a 12-month period and patients initially seen prior to the availability of WES. Data collection: retrospective chart review. Patients underwent one of the following genetic tests: karyotype, chromosomal microarray, gene sequencing of specific single genes, gene sequencing using gene

Singleton vs. trio = NR; Targeted vs whole exome analysis =NR; WES analysis and interpretation done by clinical laboratory. Diagnostic yield: any genetic testing: 34.5%; karyotype: 1/7, 14.3%; microarray: 2/12 (16.7%); single-gene sequencing: 2/13 (15.4%); epilepsy gene panel: 6/13 (46.2%); WES: 1/6, 16.7%


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sequencing panels, and/or WES. New patients 12 (48%)* were male. Age at initial evaluation was 6.8 (+/- 6.8, med: 5) years; Age at epilepsy onset (2.5 (+/- 3..1, med: 0.92) years. Diagnoses: developmental delay 96% (24); history of regression 20% (5); seizure frequency: daily 56% (14) and less than daily 44% (11); epileptic encephalopathy 56% (14) and seizure types: focal= 32% (8) and generalized = 68% (17). Established patients: 2 males mean age at epilepsy onset was 1.5 years, 2 had MRI abnormalities, 3 had daily seizures, 2 had a history of developmental regression, all had developmental delay, 3 had generalized seizures

Retterer (2016)64


U.S. 2012-2014 GeneDx, Takeda, Pathway Genomics, BioReference Laboratories

3,040 analyzed Consecutive WES cases including 17.5% (532) proband-only cases, 6.6% (200) with one additional family member, 68.4% (2,081) with two additional family members, and 7.5% (227) with three or more additional family members referred to a commercial laboratory with a wide variety of primary phenotypes including 35.6% (1,082) abnormality of the nervous system, 24.0% (729) multiple congenital anomalies, 5.7% (173) abnormality of the mitochondrion, 5.1% (154) seizures, and 4.3% (190) autisms spectrum disorders, mean proband age was 11.4 years (standard deviation = 13.2 years)

Mix of singleton and 2 other family members (trio if available, or up to two additional affected family members if available) 532/3040 (17.5%) proband only 200/3040 (6.6%) proband + 1 additional family member 2081/3040 (68.4%) proband + two additional family members (most often trio but not necessarily) 227/3040 (7.5%) proband + 3 or more additional family members Whole exome analysis ACMG interpretation guidelines, definitive result = pathogenic or likely pathogenic in known disease gene associated with reported phenotype. Identified uniparental disomy and regions of homozygosity from WES data using custom Perl script for 80%


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homozygosity over 8Mb minimum region size. Called CNV using WES data. Confirmed UPD and CNV results by appropriate measures Did mitochondrial genome sequencing by request (1221/3040 (40%)) Novel candidate gene is a possible result Diagnostic yield = 28.8% 23.6% in proband-only cases 31.0% in cases with three family members analyzed 55% (n=11) in hearing disorders 47% (n=60) in vision disorders Definitive result = 23.6% in proband-only group Definitive result = 31.0% when 3 members of family had WES Definitive result = 20.8% when proband age ≤ 30 years Definitive result = 32.0% when proband age ≤ 12 months Candidate gene result as only finding in 7.6%

Roche (2019)76 Single-arm observational cohort

U.S. 2013-2013 NIH 622 participants at enrollment, 335 eligible; 171 control group, 155 decision group

1:1 ratio randomized-controlled trial. Control group did not receive education about nonmedically actionable secondary findings (NMASF) and was not eligible to request NMASF. Decision group received education about NMASF. Study focuses on participants randomized to the decision group. 9 participants in decision group

WES procedures/information not reported in this paper. NMASF reporting procedures: A or B, telephone; C, D, or E in-person visit; F, in-person visits


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failed to attend the disclosure visit and were excluded leaving 155 participants. The sample was moderately ethnically diverse (21% Hispanic and/or nonwhite). Approximately 75% were female and the average participant age was 47 years. Participants were eligible to request up to six categories of NMASF (A) single-nucleotide polymorphisms for risk assessment of common diseases. (B) pharmacogenomic variants, (C) heterozygous variants indicating carrier status (D) specific alleles of the APOE gene associated with risks for Alzheimer disease, (E) variants associated with rare Mendelian diseases for which no effective pre-symptomatic interventions exist, and (F) variants associated with rare, highly penetrant, progressive, neurodegenerative Mendelian diseases that cannot be prevented or effectively treated Participants could request some types of NMASF without having to request them all

Rosell (2016)62 Qualitative research design

U.S. NR NR 24 parents contacted and invited to participate. Each set of parents elected to only have one parent

Interviews with parents whose child had undergone WES and had results returned in Duke Genome sequencing clinic in accordance with protocol including evaluation by medical geneticists and pre- and post-WES counseling.

Trio WES Unclear if targeted or whole exome analysis, but suspect whole exome based on reporting of incidental findings NR reporting variants in analysis Diagnostic categories were: definite, likely, partial, possible, and no diagnosis Diagnostic yield:


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participate. 19 parents were consented and interviewed.

16/19 (84.2%) female parents interviewed 19/19 (100%) Caucasian 19/19 (100%) non-Hispanic age categories 31-35 years: 5/19 (26.3%) 36-40 years: 2/19 (10.5%) 41-45 years: 7/19 (36.8%) 46-50 years: 3/19 (15.8%) 51-55 years: 2/19 (10.5%)

Definite = 2/19 (10.5%) Likely = 6/19 (31.6%) Partial (definite or likely) = 3/19 (15.8%) Possible with VUS of interest = 3/19 (15.8%) No diagnosis = 5/19 (26.3%) No comparator testing

Sawyer (2016)55


Canada NR Canadian Institute of Health Research

362 families were submitted to project for WES, 105 enrolled

A retrospective study of patients enrolled in the FORGE project (Finding of Rare Disease GEnes) were ascertained by physicians, mainly geneticists from 21 participating academic centers across the country. Diseases studied included neurodevelopmental phenotypes and dysmorphic syndromes. Patients underwent WES at one of three centralized Genome Canada Science and Technology Innovative Centers. The success rate ranged from 12% (immunological disorders) to 44% (ciliopathies). These patients had already received standard of care genetic evaluation and diagnostic testing. Patients were accepted with either a recognized clinical diagnosis (Dubowitz syndrome, etc.) or with a description of their clinical presentation (microcephaly, short stature, etc.)

Singleton Whole exome analysis Variant filtering using internal exome database for MAF < 1% diagnosis required variant in gene previously known to cause disease Diagnostic yield: 105/362 (29%) neurodevelopmental phenotypes: 31/98 (31.6%) dysmorphic syndromes: 18/80 (22.5%) ocular: 11/40 (27.5% metabolic: 12/31 (38.7%) neuromuscular: 7/30 (23.3%) ciliopathy: 12/27 (44.4%) congenital malformation syndromes: 4/19 (21.1%) immunological: 2/17 (11.8%) other: 8/20 (40.0%) Comparator: NA

Schofield (2017)26

Controlled (two or more groups) observational cohort

Australia 1998-2013 National Health and Medical Research Council of Australia and European

58 enrolled, 56 analyzed

Patients seen over 15 years at a publicly funded tertiary neuromuscular center and identified through clinical records and Muscle

Traditional diagnostic pathway included creatine kinase, nerve conduction studies, MRI imaging of brain/muscles, metabolic investigations, genetic investigations as first-


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Union Collaborative Research Grant Scheme

research Biospecimen Bank. Phenotype: 38 (67.9%) were diagnosed with congenital muscular dystrophies (CMD) and 18 (32.1%) were diagnosed with nemaline myopathy (NM). Female: 26(46.4%). Age at onset: Birth: 34 (60.7%) 1st year: 11 (19.6%) 2nd year: 8 (14.3%) >2nd year = 3 (5.4%). Congenital Muscular Dystrophy patients were included if their presentation was consistent with CMD elevated CK level >200 and muscle biopsy showed dystrophic changes or nonspecific myopathic findings. Nemaline myopathy patients were included if their presentation was consistent with a congenital onset or childhood-onset myopathy and muscle biopsy showed nemaline rods

tier tests, with muscle biopsy, protein-based studies of muscle biopsy specimens, candidate gene sequencing and CMA as second tier tests. Neuromuscular gene panel included traditional pathway followed by commercially available panel of 464 genes among those who remained undiagnosed; this pathway was used prior to muscle biopsy in the traditional pathway WES pathway included traditional testing followed by singleton WES and then trio WES if remained undiagnosed; type of analysis (whole exome vs. targeted) not explicitly stated, but likely whole exome analysis. This pathway was used prior to muscle biopsy in the traditional pathway. Diagnostic yield: Traditional pathway: 26 (46%*) Neuromuscular gene panel: pathway 42/56 (75%*) WES pathway: 44/56 (78.6%*)

Shamriz (2016)53

Case series Israel NR Hebrew University and Hadassah Medical Center

6 patients included

WES was utilized in six patients with malignant infantile osteopetrosis (MIOP) and identified mutations in four MIOP-related genes. Of six patients included, five were born to consanguineous families. In four children, the initial clinical presentation included blindness. Median and mean ages at disease onset were 1 and 13.4 (range 0.5 - 72) months, respectively. Family history of osteopetrosis was recorded in four out of six children. 4

Singleton WES Not explicit about targeted vs whole exome, suspect whole exome analysis based on "the choice of using WES rather than deep sequencing of an osteopetrosis-specific panel..." Excluded heterozygous variants in patients with consanguinity. Excluded variants with MAF >0.05 in ExAC or >1% in Hadassah in-house database. Excluded if predicted benign by Mutation Taster. Diagnostic yield: 6/6 (100%*)


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(67%)* male patients; 2 (33%)* female patients. In cases where more than one child from the same family was affected, WES was performed on one of the children and the genetic findings were confirmed by Sanger sequencing in the siblings and other family members

Shashi (2015)74 Single-arm observational cohort

U.S. 2011 - 2013 NR 188 patients eligible for chart review, 93 (49.5%) enrolled and underwent WES during period of review,

93 patients who underwent clinical WES Of participants enrolled, 53 (57%) were male, 66.7% Caucasians, 9.7% African Americans, 11.8% Hispanic, 5.4% Asian and 6.5% Others. The mean age was 7.59 +_8.08 years, ranging from newborn to 48 years. The majority of patients were younger than 18 years of age (n=85, 91.4%)

Four clinical laboratories performed the WES with 35 (37.6%), 49 (52.6%,) 6 (6.4%) and 3 (3.2%), respectively. Trio WES in 68 of 93, Proband and mother in 19 of 93, proband only in 6. Whole exome analysis; clinical laboratory's interpretation of variants. Diagnostic yield, laboratory interpretation: 24/93 (25.8%) Diagnostic yield, lab + clinician: 22/93 (23.6%)

Skinner (2018)59


U.S. NR NIH 32 Adults (n=21) and children (n=11) with uncertain exome sequencing results at an academic tertiary health care center. Study population included: 15.6% (5)* with cancer, 6.3% (2)* cardiogenetic disease, 37.5% (12)* neuromuscular or neurocognitive conditions, 9.4% (3)* ophthalmological disorders 31.3% (10)* intellectual disability and/or congenital malformations, adult mean age: 50 years (range: 19 to 84 years), child mean age: 6.5 years (range: 1 to 16 years), 68.8% (22)* female, 84.4% (27)* White

Exome sequencing. Singleton versus trios, NR. Targeted vs. whole exome, NR. Results classified as diagnostic, possibly diagnostic/uncertain, or negative determined by panel of clinical experts

Snoeijen-Schouwenaars (2019)32


Netherlands 2016 to NR NR 100 enrolled and analyzed

Diagnostic WES performed in all patients who were undiagnosed by previous targeted DNA diagnostic

Preferentially trio (66/100 (66%*), 34/100 (34%) was singleton due to either consent or DNA availability lacking for both parents.


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tests and clinical indication for WES. Patients referred to multidisciplinary outpatient clinic at Academic Center for Epileptology at Kempenhaeghe, Heeze, the Netherlands or Maastricht University Medical Center, Maastricht, the Netherlands 61/100 (61%*) with neuropsychiatric symptoms, 51/100 (51%*) with no family history of ID or epilepsy, 25/100 (25%*) with dysmorphic features, 32/86 (37.2%*) with abnormal brain MRI (denominator=MRI performed) Female = 45/100 (45%*) 98/100 Caucasian (98%*), 2/100 (2*) African mean age = 24.1 years, SD = 16.2, range = 2.8-67.6 consanguinity NR

Segregation analysis done when possible) Targeted (known epilepsy and/or ID genes) first, and those with negative targeted underwent whole exome interrogation Sanger confirmation of putative causal variants. Variants classified from Dutch guidelines for pathogenicity evaluation and interpreted according to ACMG guidelines 25/100 (25%*) patients had pathogenic/likely pathogenic by ACMG criteria from WES. 6/49 (12.2%*) from Epilepsy gene panel, 2/17 (11.8%*) from intellectual disability panel, 10/34 (29.4%*) from combined epilepsy/intellectual disability panel. 56/100 proceeded to whole exome analysis (26 not consented for this), and 7/56 (12.5%*) undiagnosed by panel testing who went on to whole exome testing were diagnosed by whole exome Previous diagnostic investigations: 4/100 (4%*) none, 9/100 (9%*) specific DNA tests, 32/100 (32%*) genome-wide chromosomal analysis, 27/100 (27%*) had specific DNA tests + genome-wide chromosomal analysis, 4/100 (4&*) had specific DNA + metabolic screening, 2/100 (2%*) had chromosomal + metabolic screening, and 22/100 (22%*) had DNA + chromosomal + metabolic screening

Soden (2014)25 Single-arm observational cohort

U.S. NR NIH, Marion Merrill Dow Foundation, Children's Mercy Kansas City, Patton Trust, William T.

119 children (from 100 families)

85 families followed in ambulatory clinics at a children's hospital with children with neurodevelopmental disorders (global developmental delay, intellectual disability,

Trio WES; variant classification as defined by ACMG. Diagnostic yield: 38.8% (33 of 85 families; data not reported by participant)


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Kemper Foundation, Pat and Gil Clements Foundation, Claire Giannini Foundation, Black and Veatch

encephalopathy, muscular weakness, failure to thrive, microcephaly, developmental regression). Mean age was about 7 years at enrollment

Srivastava (2014)57


U.S. 2011-2014 NR 78 enrolled Retrospective cohort study of patients with neurodevelopmental disabilities and unrevealing workup prior to WES. Mean patient age 8.6 +-5.8 years (range =1.6 - 26.3 years); 53% (41) were male. Family history, 14% (11) had >/= 1 affected sibling with the same phenotype, 3% (2) had an affected parent, and 12% (9) were born to consanguineous parents..

Singleton vs. trio NR Whole exome analysis Diagnostic yield was 41% (32 of 78). WES analysis was performed by outside diagnostic laboratories No comparator pathways

Stark (2016, 2017, 2019)13-16


Australia 2014-2016 Melbourne Genomics Health Alliance, State Government of Victoria, Bioplatforms Australia

89 eligible; 80 enrolled

Children age 0 to 2 years with suspected monogenic disorders (multiple congenital abnormalities, dysmorphic features, or others highly suggestive) prospectively recruited from a single tertiary pediatric center. Excluded if had specific clinical presentations that were genetically clear (e.g., achondroplasia) unless test for that disorder was not commercially available. Excluded if undergone previous sequencing tests or novel phenotypes. All participants had undergone CMA with negative results. Female 30 (38%) Age at enrollment 0-6 months: 37 (46z%)

Singleton WES as a first-tier evaluation with pathogenic and likely pathogenic variants confirmed by Sanger sequencing. in parallel with standard non-WES investigations. Analysis was limited to genes known to cause monogenic disorders (i.e. the “Mendeliome” panel). Only variants relevant to phenotype were assessed for pathogenicity. Standard clinical care: basic investigations (biochemical, imaging, neurophysiological studies, subspecialist assessments); complex investigations (biochemical testing in specialized laboratories, invasive tissue biopsies), commercial single-gene or multigene panel sequencing. Sanger sequencing of single genes, methylation studies, mitochondrial mutation panels. For participants without a diagnosis,


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6-12 months: 25 (31%) 12-36 months: 18 (23%)

sequences were reanalyzed every 6 months against the updated bioinformatics database for up to 18 months. 43 (53.8%) diagnosed after 1 round of WES testing; 47(58.8%) diagnosed after reanalysis by 18 months. [1 participant diagnosed by standard pathway and not WES] Counterfactual models for cost comparison: 1) WES as last resort, 2) WES replacing some tests, 3) WES replacing most tests

Stark (2018)22 Controlled (two or more groups) observational cohort

Australia 2016-2017 Melbourne Genomics Health Alliance and the State Government of Victoria, Bioplatforms Australia

40 in rapid WES cohort; 40 in standard WES cohort and in historical standard WES cohort.

Acutely ill infants and children with suspected monogenic disorders from two tertiary pediatric hospitals; participants received either rapid singleton WES (n=40) or standard turnaround WES (n=80). Female Rapid WES: 45% Standard WES: 37.5% Median (IQR) age at enrollment Rapid WES: 28 days (12 to 204) Standard WES: 271 days (77 to 409) Principle phenotypic feature: Congenital abnormalities and dysmorphic features Rapid WES: 22%43% (17) Standard WES: 54% Neurometabolic disorder Rapid WES: 43% Standard WES: 24% Other Rapid WES: 35% Standard WES: 22%

Singleton WES not explicitly stated as targeted vs whole exome analysis, but given diversity of phenotypes likely whole exome analysis, with variants in customized gene list prioritized for each patient; only variants relevant to a particular phenotype assessed with regard to pathogenicity. Variants classified according to ACMG and reviewed by expert panel. Diagnostic yield of rapid WES: 21 of 40 (53%) Diagnostic yield of standard WES: 25 of 40 (58%) Historical diagnostic yield of standard WES cohort prior to WES testing: 7 of 40 (17.5%)

Strauss (2017)60


U.S. 1998-2015 Charitable contributions from Old Order Amish

79 probands identified 7 diagnosed by

Clinic for Special Children, medical home for children of Old Order Amish and Mennonite populations.

68/79 (86%) had CMA array to detect CMVs. Those with uninformative CMA went on to receive WES.


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and Mennonite Communities of Pennsylvania and surrounding states Howard Hughes Medical Institute

molecular karyotype 72 analyzed using WES

Presented for evaluation between September 1998 and 2015 with clinical signs of underlying genetic disorder and remained undiagnosed following biochemical and genetic investigations. All except 3 probands were from Old Order Amish or Mennonite founder populations. 64% probands had central nervous system disease 7% had auditory or visual impairment 6% had neuromuscular weakness 5% had growth delay 4% had hepatopathy 4% had skeletal dysplasia of 52 probands with neurological disease, 85% had developmental delay (diverse phenotypes), 73% global developmental delay/intellectual disability, 60% motor disability with or without hypotonia, 44% executive dysfunction, 44% epilepsy, 27% autism, 17% extrapyramidal movement disorders, 15% affective illness. Of probands with developmental disability, 23% had microcephaly, 12% macrocephaly, and/or 13% cortical malformation. female: 36/79 (45.6%*) probands

WES performed in eligible probands and all available members of nuclear family and relevant additional family members. Whole exome analysis. Called variants filtered to MAF <= 0.01 within public, RGC internal, and CSC-population specific allele frequency databases, then annotated using publicly available annotation algorithms (e.g., SIFT). Primary analysis performed using RGC's trio-based pipeline and then refined with segregation analysis incorporating additional family members who underwent WES. ACMG guidelines used for pathogenicity determinations. Pathogenic and likely pathogenic used. Validated in CLIA lab prior to reporting. 37/72 (51%) probands subjected to WES were diagnosed using WES. 5/72 (7%) received negative WES that was considered to exclude monogenic disease. Comparator: Uses previously published costs of testing strategies to calculate cost per molecular diagnosis of "standard approach" vs theoretical genomic evaluation


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identified age: mean 6.9 years, +/- 9.4, range newborn-49.8 years (probands identified)

Tammimies (2015)72


Canada 2008- 2013 Autism Speak Canada; Autism Speaks; NeuroDevNet; Canadian Institute for Advanced Research; Univ of Toronto; Genome Canada and Ontario Genomics Institute; Canadian Institute of Health Research; Ontario Brain Institute; Hospital for Sick Children; Janeway Research Foundation

258 enrolled, 95 analyzed further after quality control

The study sample included children who were consecutively referred from both of the developmental pediatric clinics in the province that perform multidisciplinary team assessments for Autism Spectrum disorders (ASD). Each assessment was led by a developmental pediatrician. Age at diagnosis = 4.5 (mean 2.8); boys = 216 (84%)* girls = 42 (16%)* Autism Spectrum Disorder subtypes: Asperger = 27 (10.5%); Autistic disorder = 143 (55.4%); Pervasive developmental disorder, Not otherwise specified = 88 (34.1%). 95 probands were analyzed further after quality control of WES data. 8 (8.9%) children with 9 mutations received and ASD molecular diagnosis

Trio Not explicit about targeted vs whole exome, incidental findings therefore whole exome analysis probable Diagnostic yield: 8/95 (9.4%) Comparator: chromosomal microarray 24/258 (9.3%) received molecular diagnosis from chromosomal microarray. 15/95 who underwent both CMA and WES (15.8%) received diagnosis. 2/95 who underwent both CMA and WES received molecular diagnosis from both tests

Tan (2017)19 Single-arm observational cohort

Australia 2015-2015 Melbourne Genomics Health Alliance, State Government of Victoria, Australian Genome Research Facility sponsored by Bioplatforms Australia

61 assessed for eligibility 3 excluded for novel phenotype 7 enrolled in another genomic project 5 declined or withdrew consent

Tertiary health care center Prospective recruitment of ambulatory children aged 2-18 years suspected of having monogenic condition Recruited from outpatient clinics of Victorian Clinical Genetics Services at Royal Children's Hospital, Melbourne, Australia May 1 to November 30, 2015 Panel of experts determined

Singleton WES Targeted; analyzed only variants in HUGO Gene Nomenclature Committee genes associated with mendelian disease before the end of 2015 (3203 genes) Variants assessed using Melbourne Genomics variant curation database, a modification of Leiden Open Variation Database, prioritized based on phenotype-driven gene lists for each participants (Gene Prioritization Index) and predicted effect


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2 diagnosed by microarray 44 enrolled and analyzed

eligibility Excluded those whose diagnosis usually made by clinical assessment (e.g., achondroplasia or neurofibromatosis type 1) All had previous nondiagnostic SNP microarray but no prior single-gene or panel sequencing tests. Methylation or triplet repeat analysis allowed. Excluded children deemed to have novel phenotypes Primary phenotype among those enrolled and analyzed dysmorphic with multiple congenital anomalies = 21/44 (47.7%*) neurometabolic = 8/44 (18.2%*) intellectual disability without congenital anomalies = 7/44 (15.9%*) skeletal dysplasia = 4/44 (9.1%*) dermatological = 4/44 (9.1%*) female = 23/44 (52%) age at enrollment 2-10 years = 30/44 (68%) 10-18 years = 14/44 (32%)

(Variant Prioritization Index) Only assessed pathogenicity of variants relevant to participant's phenotype based on ACMG standards for interpretation. Reviewed at multidisciplinary meeting. Parents underwent Sanger sequencing to confirm phase and segregation Reanalyzed unsolved cases (unsure when) Counterfactual comparator testing scenarios: 1) "standard diagnostic pathway" without WES, includes microarray 2) "standard diagnostic pathway" with WES as final test 3) WES at first genetics appointment 4) WES at initial tertiary presentation 23/44 (52%) received molecular diagnosis from WES

Tarailo-Graovac (2016)44


Canada 2012-2015 BC Children’s Hospital Foundation, BC Clinical Genomics Network, the Rare Diseases Foundation, Canadian Institutes of Health Research, British Heart

47 eligible, 41 analyzed

Consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes undergoing WES and deep clinical phenotyping at an academic medical center, median age: 5.9 years (range, 8 months-31 years), 37% (15) female, 63% (26) white

Trio + (WES done on proband, both parents, and affected siblings if available) Targeted vs whole exome analysis not explicitly stated. They allowed for novel candidate genes, so I would guess whole exome analysis Used ACMG guidelines to classify pathogenicity of variants. Novel candidate genes allowed. Diagnostic yield: 28/41 probands (68%) with


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Foundation, National Institute of General Medical Sciences, Leenaards Foundation, Rare Disease Initiative Zurich

variants either pathogenic or probably pathogenic. Includes 2 genes newly implicated in disease No comparator testing. Patients undergone biochemical testing from published diagnostic algorithm for treatable intellectual developmental disorder and clinical genetic testing but remained undiagnosed

Tsiplova (2017)79

Modeling Study Canada 2013-2014 Genome Canada and Ontario Genomics Institute, Centre for Applied Genomics and Genome Diagnostics at the Hospital for Sick Children

NA, synthetic population for cost modeling

Modeling study using a bottoms up micro-costing approach from an institutional perspective based on the laboratory practices at the Hospital for Sick Children, Canada. The target population approach was children in the referral and diagnostic pathway for ASD

Model assumptions: Singleton WES, assumed follow-up Sanger sequencing for proband and 2 parents in 50% of cases Diagnostic yield assumptions On average 2 variants per participants (range 0 to 4) 3 to 5% with secondary (incidental) findings Strategies evaluated: CMA alone CMA + WES

Valencia (2015)54


U.S. NR National Human Genome Research Institute

40 pediatric cases, 12 (30%) had genetic defects

Retrospective review of 40 pediatric patients referred by medical specialists (medical geneticists 77%, Immunologists 15%, Cardiologists 3% and others 3%) for exome sequencing. The patients in this cohort had diverse clinical features: 30% congenital anomalies, 22% neurological disorders, 17% immunodeficiencies, 25% mitochondrial disorders All patients were under 17 years of age at time of exome analysis (average age 83.2 months) and much younger at the time of clinical

Singleton Whole exome analysis Diagnostic yield: 12/40 (30%) Used ACMG guidelines for category 1 or 2 Scrutinized putative causal variants in literature review, used in silico prediction programs and Sanger sequencing for familial segregation Defined full molecular diagnosis as gene variant(s) classified as pathogenic or likely pathogenic that explains most/all of clinical features Partial molecular diagnosis equaled gene variant(s) classified as pathogenic or likely pathogenic that explains one or several


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presentation (average age 5.3 months). Prior to referral, all patients had undergone extensive diagnostic evaluations. Males 27 (68%)* and females (32%) *; 37 (97%)* Caucasian; 14/40 (36.7%*) non-Caucasian or of mixed ethnicity, including the individual with Ashkenazi ancestry, who would potentially need different reference panels

clinical features. Comparator: NA

Vanderver (2016)71


Australia 2009-2013 Illumina Inc. 191 cases identified; 71 families enrolled

71 patients with persistently unresolved white matter abnormalities with a suspected diagnosis of leukodystrophy or genetic leukoencephalopathy. WES analyses performed on trio, or greater family groups. Patients had high quality samples available for complete trios. Patients included 30 female and 47 male individuals who all had abnormal white matter signal on neuroimaging. Individuals ranged in age from 3 years to 26 years at the time of sequencing, but symptom onset ranged from birth to 19 years. Ethnicities varied and included individuals of mixed and northern European descent, as well as African American, Arab, African, Asian, and Latin American origin

Trio or trio + additional family members Not explicitly stated targeted vs whole exome Used custom variant annotation and interpretation software to identify causal mutations Interpretation included disease association in public database or published literature ACMG criteria for pathogenic or likely pathogenic mutations in known disease genes and clinical feature correlation with disease were classified as "diagnostically resolved" Diagnostic yield: 25/71 (35%) Potentially pathogenic variants: 5/71 (7%) "clinical diagnoses" =42% Comparator testing pathway: NA

Vissers (2017)20


Netherlands 2011-2015 Netherlands organization of Health Research and Development

150 consecutive patients with nonacute neurological symptoms of suspected

150 consecutive patients with nonacute neurological symptoms of suspected genetic origin selected. Referred by GP (n=11), medical specialist (n=55), previously referred but remained undiagnosed (n=84). Tertiary referral center.

Singleton WES in 7/150 patients Trio 143/150 patients 1st step was in-silico panel test using WES sequencing, 2nd step looked at variants outside the panel WES "panel" determined by presenting phenotype


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genetic origin selected.

Excluded with well-known, clinically diagnosable disorders (e.g. NF1) Median age: 5years 7months (range: 5 months to 18 years) 53.3% male 78 intellectual disability (52%*) 20 movement disorders (13.3%*) 8 neuromuscular disease (5.3%*) 5 epilepsy (3.3%*) 39 combination of above (26%*)

Performed variant calling for SNV and CNV Standard pathway was determined at discretion of pediatric neurologists, may have included single-gene tests and arrays Standard pathway and WES received in parallel and patients followed for a minimum of 6 months after starting WES (median 17mo, range 6-42months) Diagnostic yield by WES = 44/150 (29.3%) Diagnostic yield by standard pathway = 11/150 (7.3%)

Vrijenhoek (2018)77


Netherlands 2015 European Union's Horizon 2020 research and innovation programme

370 Retrospective study that analyzed medical records of 370 patients with intellectual disabilities (ID) who had undergone WES at various stages of diagnosis at the Wilhelmina Children Hospital, University Medical Centre, Utrecht Age, sex, and race/ethnicity: NR

Trio WES; targeted vs whole exome analysis not explicitly stated ESHG recommendations informed variant filtering Diagnostic yield: 128 (35%)

Waldrop (2019)47


U.S. 2013-2017 NIH 31 patients, 30 families

Pediatric patients seen in a neuromuscular clinic who had WES performed since 2013, WES performed at Baylor or Gene Dx

Trio sequencing Report provided to ordering clinician only included genes predicted to be related to patient's clinical phenotype + medically actionable variants, ACMG/ACOG carrier status guidelines WES performed (due to presence of incidental findings) Pathogenic, likely pathogenic, or VUS. Focused report on genes predicted to patient's clinical phenotype Diagnostic yield: 11/30 (37%) of families got genetic diagnosis No comparator testing, but range between 2-12 prior genetic tests before going to WES


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Walsh (2017)78 Single-arm observational cohort

Australia 2014-2015 Melbourne Genomics Health Alliance, State government of Victoria, Bioplatforms Australia

53 eligible, 50 enrolled, and analyzed

50 adults and children with peripheral neuropathies prospectively enrolled by neurologist, genetics, genetic counselor at Royal Children's Hospital or Royal Melbourne Hospital. All had neurophysiologically confirmed peripheral neuropathy of likely monogenic cause. If suspected based on clinical symptoms, CMT1A from PMP22 duplication was excluded using non-WES analysis prior to study enrollment Female: 17 (34%) Race/ethnicity: NR Age: median: 18 years, range 2-68 Phenotype Demyelinating sensorimotor neuropathy: 9 (18%) Axonal sensorimotor neuropathy: 17 (34%) Intermediate sensorimotor neuropathy: 10 (20%) Pure motor neuropathy: 11 (22%), Pure sensory neuropathy: 3 (6%)

Singleton WES initially targeted to 55 genes associated with peripheral neuropathies as of 2013; uninformative patients expanded to 88 gene panel plus a SNP array; patients with additional syndromic features had customized gene panel generated. If all else failed, variants from whole exome analysis considered. Variants classified by ACMG standards, discussed by expert panel, and confirmed with Sanger sequencing. Family segregation studies done as needed. Diagnostic yield Initial 55 gene panel: 12 (24%) SNP Microarray after undiagnosed on initial panel: 2 of 38 remaining undiagnosed (37) or in case where 2nd diagnosis suspected (1) Expanded WES analysis: 8 of 36 remaining undiagnosed (22%), cumulative diagnostic yield 20 of 50 (40%) Comparator strategy: Hypothetical scenario: WES replaces sequencing-based genetic tests, repeated nerve conduction studies, complex biochemical tests, and tissue biopsies. Limits diagnostic neurology appointments to 2/patient

Werner-Lin (2018)68


U.S. NR National Human Genome Research Institute

10 Interviews with adolescents (aged 12 to 19 years at recruitment) and their parents recruited from disease specific clinics, phenotypes included: 60% (6) cardiac arrhythmia, 20% (2) hearing loss, and 20% (2) platelet disorders, 30% (3) aged 12 to 15 years and 70%(7)

Type of WES: NR Targeted vs whole exome: NR Variant reporting: allowed carrier variants and VUS Diagnostic yield = 3/10 (30%*) but this isn't a meaningful number Comparator testing: NA


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aged 16 to 19 years, 60% (6) female, 70% (7), White

Willing (2015)56 Single-arm observational cohort

U.S. 2011 - 2014 Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Human Genome Research Institute, National Center for Advancing Translational Services

49 enrolled, 35 infants eligible

Retrospective comparison of STATseq and standard genetic testing in a case series from the level 4 NICU and PICU of a quaternary children's hospital. The participants were families with an infant younger than 4 months with an acute illness of suspected genetic cause and did not have a genetic diagnosis. Study compared diagnostic rate, time to diagnosis, and types of molecular diagnoses of standard clinical genetic testing. Affected children were nominated for STATseq by the treating physician, typically a neonatologist. 18 (51%) were male; median age at enrollment: 26 days/ range (1-71 days) Clinical features of affected infants were ascertained through physician and family interviews and review of medical records. STATseq was done in the lab at Children's Mercy- Kansas City on both parents and affected infants simultaneously. Principal phenotypes included: multisystem congenital anomalies = 9 (26%); neurological = 7 (20%); cardiac or heterotaxy = 5 (14%); hydrops or pleural effusion = 4 (11%); metabolic findings, including hypoglycaemia = 4 (11%); renal = 1 (3%); Arthrogryposis = 2 (6%);

STATseq = rapid WGS STATseq of trios Whole exome analysis Identified causative variants using VIKING software Classified as definitive diagnosis if ACMG pathogenic or likely pathogenic in disease gene that overlapped with reported phenotype in medical record Sanger sequencing to confirm likely causative Diagnostic yield: 20/35 (57%) by STATseq Comparator: standard genetic testing based on clinical judgment (may have included gene panel sequencing), yield: 3/32 (9%)


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respiratory = 1 (3%); hepatic = 1 (3)%; dermatological = 1 (3%) Of the 35 infants who had STATseq, 32 had standard genetic testing based on physician's clinical judgment

Yang (2014)66 Single-arm observational cohort

U.S. 2012-2014 National Human Genome Research Institute

2000 analyzed Clinical WES at Whole Genome Laboratory of Baylor College of Medicine, CLIA-certified, tertiary care center 2000 consecutive patients with WES ordered by patient's physician. Exclusion was for financial reasons only. mean age 6 years. Categorized as "neurological" (n=526) , "neurological plus other organ systems" (n=1147), "specific neurological" (n=83), and "non-neurological" (n=244) Could have had previous workup that did not yield molecular diagnosis phenotype = 82.2% had nonneurological female = 888/2000 = 44% (11 fetuses with gender unknown, 1%) race/ethnicity NR 900/2000 (45%) <5years, 845/2000 (42.2%) 5-18years, 244/2000 (12.2%) adults >18years, and

Singleton Whole exome Molecularly diagnosed defined as pathogenic or likely pathogenic variant detected in Mendelian disease genes that overlapped with described phenotype, and biallelic variants required for recessive disorders. 2 tiers of reporting: tier 1 included 1) pathogenic variants related to disease phenotype, 2) VUS related to disease phenotype, 3) medically actionable mutations including ACMD 56, 4) carrier status for ACMG-recommended population screening panel, 5) defined number of pharmacogenetic variants, and 6) clinically relevant mitochondrial mutations. Tier 2 included deleterious mutations or VUS unrelated to disease phenotype, and predicted deleterious mutations in nondisease genes Diagnostic yield: 504/2000 = 25.2% overall, 143/526 (27.2%) neurological, 282/1147 (24.6%) neurological + other organ systems, 30/83 (36.1%) specific neurological, 49/244 (20.1%) non-neurological Diagnostic yield excluding fetuses: Overall = 498/1989 (25%*), neurological =143/526 (27.2%), neurological + other organ systems


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11/2000 terminated fetal samples (0.6%)

= 277/1140 (24.3%*), specific neurological = 29/82 (36.3%*), non-neurological = 49/241 (20.3%*) Comparator testing: NA

Zhu (2015)45 Single-arm observational cohort

U.S. NR UCB Celltech

119 patients analyzed 113 first analyses 6 re-analyses

65 trios recruited from Genome Sequencing Clinic at Duke University Medical Center, (54.6%*) 48 trios recruited from Sheba Medical Center in Tel HaShomer, Israel (40.3%*) 6 trios previously recruited and unresolved (0.05%*), I think from Duke 113/119 trios reported for the first time 6/119 trios reinterpreted (previously unresolved) mean age = 9.5*, sd = 8.7* female: 52/119 = 68.1* Clinical phenotypes vary widely

Trio WES Whole exome analysis Variant reporting: used two independent sources of population controls: Center for Human Genome Variation at Duke controls (phenotypes not analyzed), and NHLBI Grand Opportunity Exome Sequencing Project. Qualifying genes from analysis checked against OMIM for phenotypic overlap, consistency with inheritance pattern, similarity of mutation in reported data Diagnostic yield 29/119 = 24% No comparators

Abbreviations: ACMG = American College of Medical Genetics; CI = confidence interval; CMA = chromosomal micro-array; CNV = copy number variant; EEG = Electroencephalographic; FH = familial hypercholesterolemia; ID = intellectual disability; NA = not applicable; NGS = next-generation sequencing; NICU = neonatal intensive care unit; NMASF = nonmedically actionable secondary findings; NR = not reported; SD = standard deviation; SNP = single-nucleotide polymorphisms; UPD = uniparental disomy; VUS = variance of unknown significance; WES = whole exome sequencing; * = calculated value


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Table C-2. Clinical Utility Outcomes

Author (Year) Risk of Bias

Number and Proportion of Participants with a Potential Change in Management

Number and Proportion of Participants with an Actual Change in Management

Number and proportion of participants with results Leading to Additional Genetic Counseling or Testing in Family

Balridge (2017)43 Some

NR 8 (12% of those with a diagnosis, 5% of those tested) had directly altered clinical care

18 (11% of those tested, 26% of those diagnosed)

Bourchany (2017)42

High

2/13 (15.4%*) of those with WES diagnosis had change in prognosis. 6/13 (46.2%) of those with WES diagnosis had change in inheritance pattern of presumed diagnosis

1/13 (7.7%*) of those with WES diagnosis had investigation of systemic involvement. 2/13 (15.4%*) of those with WES diagnosis included in clinical trials

12/13 (92.3%*) of those diagnosed using WES received prenatal counseling/testing

Cordboba (2018)18

Some

NR 43.8%* (7of 16) of those with a diagnosis 17.5%* (7 of 40) of those tested 6.3%*(1) of those with a diagnosis had endocrine monitoring 25%*(4) of those with a diagnosis were treated with a with new medication 12.5% (2) of those with a diagnosis were advised to avoid a medication

NR

Daga (2018)41

Some

46%* (7 of 15) of families with diagnosis, (14%* of families tested) received a diagnosis that resulted in a potential change in treatment

20% (3 of 15) of diagnosed families received screening for other symptoms of their genetic disease

NR

Evers (2017)52

Some

NR 8 (38%) of 21 cases had management changes 2 (8%) change in medication or biotherapy 7 (33%) began surveillance for disease complications

20 (95%) of 21 said results were important for family planning 19% (4 cases in 21 families) have used results for prenatal diagnosis

Hauer (2017)51

High

NR 31 families (15.5% of 200 exome individuals) led to preventive measures 23 families (11.5%) orthopedic support and developmental evaluation 9 families (4.5%) had recommendations for symptomatic treatment or screening for associated malformations 4 families (2%) received new medications to treat their specific genetic defect

NR

Howell (2018)28

High

NR Genetic diagnosis led to a management change in 1 participant (SCN2A mutation with sodium channel blocking AEDs used); unclear what % this represents since not calculable based on data reported in article

100% of those with a genetic diagnosis (unclear N); a significant recurrent risk was identified in 5 families


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Iglesias (2014)46

Some

NR Of the 37 with a diagnosis: 22%* (8) were screened for other manifestations of the disease 38%* (14) had changes in management 5%* (2) changed treatment

14%*(5 of 37) identifying other family member mutation carriers 16%* (6 of 37) reproductive planning

Jones (2018)50

High

NR Of patients with FH molecular diagnosis 18 (78% of 23) prescribed lipid-lowering therapy 8 (47% of 17) changes to intensity of medication management 9 (39% of 23) changes made to their treatment regiments 1 (11% of 9) was initiated on new therapy

8 (42% of 19) discussed genetic results with clinical genomics specialist

Mann (2019)48

Some

5 probands (4 had correct clinical diagnosis) where molecular genetic etiology had clinical consequences 5/104 = 4.8%* This is a counterfactual potential change in management, as they had already been transplanted

NR NR

Matias (2019)49

Some

NR Change from pre-WES to post-WES Any change (Not significant (NS)) 100% (37 of 37) of those with a diagnosis 95% (31 of 41) of those without a diagnosis Imaging tests (NS) 46% (17 of 37) of those with a diagnosis 56% (23 of 41) of those without a diagnosis Metabolic testing (NS) 43% (16 of 37) of those with a diagnosis 46% (19 of 41) of those without a diagnosis Genetic testing (NS) 73% (27 of 37) of those with a diagnosis 80% (33 of 41) of those without a diagnosis Received specialist referrals (p=0.05) 43% (16 of 37) of those with a diagnosis 46% (19 of 41) of those without a diagnosis

Any genetic counseling change: (p <0.001) 97% (36 of 37) with a diagnosis 5% (2 of 41) of those without a diagnosis Recurrence risk (p <0.001) 95% (35 of 37) with a diagnosis 0% (o of 41) of those without a diagnosis Reproductive counseling: (p <0.001) 97% (36 of 37) of those with a diagnosis 0% (o of 41) of those without a diagnosis Family Testing: (p <0.001) 97% (36 of 37) of those with a diagnosis 0% (0 of 0) of those without a diagnosis


Final






Lifestyle recommendations (NS, significant with 4 group analysis) 24% (9 of 37) of those with a diagnosis 12% (5 of 41) of those without a diagnosis

Meng (2017)40

Some

NR 52.0% (53 of 102) of those with diagnosis had a change in medical management 35.8%(19 of 53) of those with diagnosis had a redirection in care 50.9% (27 of 53) of those with a diagnosis had initiation of subspecialist care 13.2% (7 of 53) of those with a diagnosis a change in medication or diet 9.4% (5 of 53) of those with a diagnosis had a major procedure completed

88% (90 of 102) of families with diagnosed received genetic counseling

Niguidula (2018)39

High

NR Medication change: 11% of those tested (17% of those with diagnosis, 29% of those with uncertain results, 3% of those with negative results) Discontinue diagnostic studies: 58% of those tested (96% of those with positive test, 86% of those with uncertain test, 25% of those with negative test) Medical management change: 40% of those tested (78% of those with diagnosis, 71% of those with uncertain diagnosis, 9% of those with negative diagnosis) Psychosocial support: 27% of those tested (65% of those with positive diagnosis, 29% of those with uncertain diagnosis, and 0% of those with negative diagnosis)

Reproductive planning: 45% of those tested (87% of those with diagnosis, 86% of those with uncertain results, 6% of those with negative results)

Nolan (2016)24 Some

NR 10 (18.9%) of those tested and (41.7%) of those with a diagnosis

11 (22%) of those tested and (46%) of those with a diagnosis

Palmer (2018)27

High

NR 31.3% (5) of those diagnosed had changes in management 6.3% (1) of those with a diagnosis had palliative care initiated 6.3% (1) of those with diagnosis had reduced invasive/costly diagnostic investigations 6.3 % (1) had targeted management (not specified)

43.7% (7) of those with diagnosis had reproductive planning


Final






12.5% (2 ) of those with a diagnosis received guidance on AED therapy

Perucca (2017)34 High

NR 1/5 of those with molecular diagnosis (20%*) had a change in medication

NR

Ream (2014)58

Some

0/6 (0%) of patients diagnosed by WES 4/23 (17%) patients diagnosed by other genetic tests had diagnoses defined a priori as having potential therapeutic implications

0/6 (0%) of patients diagnosed by WES 13%* (3/23) patients diagnosed by other genetic tests had a change in medication. 4%* (1/23) patients diagnosed by other genetic tests was prescribed a special diet

50% (3 of 6) of WES patients received genetic counseling regarding the implications of heterozygous autosomal recessive mutations with potential diagnostic significance

Sawyer (2016)55

High

NR 6 (26%) of 105 families 3 had adjustment of therapy and 3 had therapy initiated

NR

Shamriz (2016)53

High

NR One patient, decision to defer allogenic hematopoietic stem cell transplantation based on clinical and genetic findings, treatment included palliative care only

NR


High

10/25 (40%) had a potential change in management from their WES results 5/25 (20%) of those with pathogenic/likely pathogenic variance had potential consequence for clinical approach An additional 5/25 (20%), variants with possible consequence for daily clinical care (5/100 (5%*) of those analyzed)

1/25 (4%*) of those with pathogenic/likely pathogenic variance had a change in management (medication change) resulting from the WES results

NR

Soden (2014)25

High

NR 49% (22 families with a diagnosis) had a change in patient management and/or clinical impression of the pathophysiology 23% (10 families with a diagnosis) had a change in drug or dietary treatment (this either occurred or were planned) 6.7% (3 families with a diagnosis) had a discontinuation of unnecessary treatments 20% (9 families with a diagnosis) had additional evaluation for possible disease complications

NR

Srivastava (2014)57

High

NR WES testing affected management in 41% (32 of 78) of patients, in 100% (32 of 32) of those with a presumptive diagnosis 5% (4) started disease monitoring after diagnosis 6% (5) discontinued medication

135%* (27 of 78) of patients had results essential for reproductive planning


Final






3% (2) started medication 8% (6) received further workup for systemic involvement

Stark (2016, 2017, 2019)13-16

Some

NR 16/47 (34%) of those diagnosed using WES, 16/80 (20%) of those tested; includes 13/16 who added new treatment or surveillance and 4/16 who stopped treatment/surveillance

Testing was offered to all available parents and some siblings where clinically indicated. 79/88 eligible first-degree relatives underwent cascade testing (including those only diagnosed with standard care); 12 relatives of WES-diagnosed probands received a genetic diagnosis from cascade testing. 5 relatives would have received genetic diagnosis if proband diagnosed using standard of care pathway; 28 couples were identified as high risk of recurrence from WES. 13 couples would have been identified using standard of care pathway (counterfactual); 14/47 (30%) of families with a WES diagnosis sought reproductive counseling services: 2 preimplantation genetic counseling, 12 prenatal genetic diagnosis. 2 (6%) of families without a diagnosis sought reproductive counseling services

Stark (2018)22

High

NR Reported for the Rapid WES Cohort Only 20% (16) overall had change in management 10%*(4) medication started/adjusted 3%*(1) medication stopped 18%*(7) surveillance initiated 0%*(0) surveillance stopped 8%*(3) avoidance of tissue biopsy 5%*(2) redirection to palliative care

NR

Tan (2017)19

Some

NR 7 (30% of those diagnosed, 16% of those tested) had change in management (specific changes unspecified) 6 (26% of those diagnosed, 14% of those tested) had 1 (4% of those diagnosed, 2% of those tested) of those stopped planned investigations

1 (4% of those diagnosed, 2% of those tested) had a prenatal implantation genetic diagnosis planned


Some

NR 44% (18) with pathogenic or probably pathogenic variant had impact on clinical treatment, including: 4 of 18 had preventive measures: regular cancer screening from patients with high risk of

NR


Final






malignancies or avoidance of disease triggers 3 of 18 immune-modulating therapies 5 of 18 more precise symptomatic treatment 7 of 18 treatments targeting the identified abnormality at a cellular or molecular level

Valencia (2015)54

Some

12.5% (5 of 40) 2 (5%) had change in management 12 (30%) altered medical management including genetic counseling

NR

Waldrop (2019)47

Some

NR 25% (3 of 12) develop plan for disease surveillance (e.g. cardiac, immune or eye) 8%* (1 of 12) discontinue medication 8%* (1 of 12) certainty of malignant hyperthermia risk 8%* (1 of 12) with diagnosis started palliative care

100% (12 of 12) of those with a diagnosis

Willing (2015)56

High

NR 12/20 (60%) had a change in management 13 (65%) of those with a STATseq diagnosis report acute clinical usefulness, 4 (20%) had diagnoses with favorable effects on management and 6 (30%) were started on palliative care

NR

Zhu (2015)45

High

NR 4/119 tested (3.4%*) 4/29 diagnosed (13.8%*) 2/119 (1.7%*) tested had specific pharmacotherapies result from WES results 2/119 (1.7%*) tested had specific diet interventions result from WES

NR

Abbreviations: AED = antiepileptic drugs; FH = Familial hypercholesterolemia; NA = not applicable; NR = not reported; NS = not significant; WES = whole exome sequencing; * = calculated value


Final


Table C-3. Health Outcomes

Author (Year) Risk of Bias Mortality Length of Survival Other health outcomes (morbidity, cognitive ability, functional outcomes)

Meng (2017)40 Some 5-yr death rate: Diagnosed: 39 of 102 (38.2%) Not diagnose:, 41 of 170 (24.1%) 120-day death rate: Diagnosed: 30 of 102 (29.4%) Not diagnosed: 28 of 170 (16.5%)

NR NR

Perucca (2017)34 High NR NR 1/5 of those with molecular diagnosis (20%) (1 of 1 with change in management) experienced change from "uncontrolled monthly seizures" to seizure-free for 12 months since implementing change in management

Ream (2014)58 High NR NR Seizure control. 0%* (0/6) of WES patients had improved seizure control. 1 of 23 (4%) patients diagnosed with other genetic tests had improved seizure control receiving stiripentol based on the gene test result

Shamriz (2016)53 High 1 patient of 6 (16.7%*) with diagnosis from WES died after parent refusal of treatment 2 years after initial diagnosis Total follow-up: 2.5 years Length of follow-up for all patients: 0.28 to 8.96 years Patients were not assessed for a minimum amount of time to record outcomes

NR 1/6 (16.7%) with diagnosis from WES experienced progressive neurological deterioration 4/6 (66.7%) with diagnosis from WES were alive and well


High NR NR 1 (4%*) of 25 patients with likely pathogenic variant had improved behavior and mood following medication change based on WES result

Stark (2018)22 Some Unclear length of follow-up 9 (23%) of rapid WES cohort 9 (11%) of standard WES cohort

NR NR

Willing (2015)56 High 14 (40%) of 35 infants died within 120 days 120-day mortality was 57% (12 of 21) in infants with a genetic diagnosis (ALM during QC: this number includes infants with a diagnosis by either STATseq or standard testing) 4 infants died within 4 days of enrollment

NR NR

Abbreviations: AED = antiepileptic drugs; NA = not applicable; NR = not reported, NS = not significant; WES = whole exome sequencing


Final


Table C-4. Safety Outcomes

Author (Year) Risk of Bias Misdiagnosis (False positives/False Negatives)

Proportion of participants with ACMG-defined medically actionable variants Psychosocial harms

Baldridge (2017)43

Some NR 141 (97% of the 146 who were given choice to opt in or out) elected to receive incidental findings. 14 (10% of those tested who opted in) had incidental findings in one or more of the 56 ACMG-defined genes

NR

Bourchany (2017)42

Low NR 0/29 (0%*) of those tested had any ACMG 56 incidental findings

NR

Ding (2014)75 Some NR Modeling confirms that 1.5%–6.5% of screened individuals will have a significant reportable finding

NR

Jones (2018)50 NA- Qualitative Study NR NR Some shock related to a participant's discovery of nonpaternity as a result of family discussions regarding family history of heart disease

Jurgens (2015)65 Some NR 2/232 (0.86%) individuals had a reportable variant in an ACMG gene

NR

Lee (2015)73 Low NR 1 of 26 (4%)* of those tested NR

Li (2019)67 NA- Qualitative Study NR NR Participants reported a range of emotions upon receiving a report of variants of unknown significance including confusion, anger, stress, fear, relief, and disappointment The majority of participants reported it did not affect their ability to take care of their child, or alter their perception of their child's condition.


Low NR NR Anxiety and Depression: 44 parents who completed GAD-7 and PHQ - 9, 29 (65.9%) did not meet criteria for depressive disorder, 8 (18.2%) had mild depression, and 7 (15.9%) had moderate depression. For anxiety 26 (59.1%) did not meet criteria for anxiety, 6 (13.7%) had moderate anxiety, and 1 (2.3%) had severe anxiety Among those whose children underwent prior WES : PHQ-9 (mean +/- sd) = 5.45 +/- 5.99 GAD-7 = 5.36 +- 4.96 CSE = 188.24 +/- 37.97 Health care engagement = 18.52 +/- 1.75 Uncertainty tolerance = 16.24 +/- 2.66

Meng (2017)40 Some NR 7.9% (21 of 267) of those who agreed to receive information

NR

Monies (2017)61 Some NR 1.2% of cohort tested (panel + WES) NR


Final




Muramatsu (2017)70

Low NR 0 of 250 (0%) NR

Nolan (2016)24 Some NR 5 (10%) of total tested Not specifically reported in the study as ACMG-defined medically actionable variants; however, study authors reported that all 5 findings affected patient management

NR

Posey (2015)63 Low NR Medically actionable findings ACMG criteria, 6/482 = 1.2% Outside of ACMG criteria findings = 6/481 = 1.2%

NR

Ream (2014)58 High NR 67% (4 of 6) WES patients had a cytochrome enzyme mutation affecting drug metabolism

NR

Retterer (2016)64 Low NR 12.2% (291of 2,382) participants opted out of receiving secondary findings 6.2% (129 of 2,091) of those who opted to receive secondary findings had reportable secondary findings

NR

Roche (2019)76 High NR 2% (13 of 622) participants ineligible as a result of medically actionable SF

5 of 36 respondents to a survey of participants who did not request nonmedically actionable results reason for not requesting results were concern that information would be an emotional burden

Rosell (2016)62 NA NR No incidental findings identified All parents hoped for diagnosis: 4/19 had high expectations of diagnosis; 13/19 had tempered expectations, not wanting to get their hopes up only to be disappointed; 2 parents had low expectations Some parents voiced frustration and disappointment with waiting and not getting complete answers Some families felt need for more follow-up counseling or outreach Some expressed need to help families manage expectations

Shashi (2016)74 Low NR 3.3% (2/59 patients tested after publication of ACMG guidelines) with an incidental mutation All patients opted to receive results

NR

Skinner (2018)59 NA- Qualitative Study NR NR Only 1/32 (3.1%) misinterpreted an uncertain result as a definitive answer. The clinicians reported it as a possible but uncertain explanation, but the patient interpreted it as definitive and


Final




described it as 'life-changing'. She did understand the VUS did not cause her symptoms when her unaffected father was found to carry the same variant. Some adult participants for whom family testing was recommended did not pursue it because they did not want to pressure family members. Patients pursuing testing did not worry while waiting on results. Some commented that uncertainty was not new. One participant reported experiencing distress related to the uncertain result. No participants expressed regret at learning the uncertain result. No participants reacted to the uncertain result in ways that could cause harm. Most regarded the information as potentially valuable in the future.

Strauss (2017)60 Low NR 490/502 (98%) subjects subjected to family-based WES elected to receive secondary findings. 21 (4.2%) subjects had 1 of 4 pathogenic/likely pathogenic variants in 3 genes (BRCA2, APOB, and DSC2) and received these results

NR

Tammimies (2015)72

Low NR 8 of 95 probands (8.4%) reported incidental findings 6 (6.2%) were deemed medically actionable

NR

Valencia (2015)54 Low 16% of variants identified by WES and carried forward to Sanger sequencing did not survive Sanger sequencing and were therefore WES false positives. However, because Sanger sequencing was part of the pipeline prior to diagnosis, they are not false positives

8%* (3) had reported medically actionable findings in 3 ACMG-recommended reportable genes (MYL2, FBN1, BRCA2)

NR

Vanderver (2016)71

Low NR Unaffected adults screened = 142 Incidental findings = 3 (2.1%) Affected children screened = 79

NR


Final




Incidental findings = 3 (3.7%) Unaffected siblings = 0/10 (0%) with incidental findings 3/71 (4.2%*) families screened had incidental findings Incidental findings included the 56 adult and 49 pediatric ACMG-recommended genes

Vissers (2017)20 Some 3 patients received diagnosis through non-WES pathway but NOT through WES (9bp duplication, repeat expansion, mosaic duplication of Chr7). 3/150 (2%*) of those tested received a false negative diagnosis by WES 36 patients (76.6%) received diagnosis through WES pathway but not through standard diagnostic pathway. 36/150 received a false negative diagnosis by standard care pathway (24%)

0/150 (0%*) tested had incidental findings (study did not define incidental findings)

NR

Werner-Lin (2018)68

NA (qualitative study) NR 10% (1) nonimmediately actionable childhood-onset finding 8/10 (80%) had positive carrier findings (in accordance with ACMG)

Families were initially disappointed when uncertain results were conveyed; they experienced frustration, disappointment, and fear. These feelings evolved over time; and moved toward acceptance and satisfaction, generally within the ensuing 3 months.

Yang (2014)66 Low NR 59/2000 (3%) ACMG-defined by local definition: 95 variants found in 92/2000 patients (4.6%) with incidental findings that had immediate implications for management

Of the 92 patients with incidental findings, 33 parents from 19 families have requested testing for medically actionable variants found in proband.

Abbreviations: NA = not applicable; NR = not reported.


Final


Table C-5. Characteristics of Included Studies Reporting Cost Outcomes

Author (Year) Cost Study Design

Year and Unit of Currency Reported

Perspective Used

Time Horizon and Discounting Description of Costs Included

Description of Benefit and/or Utility Measures Used

Cordoba (2018)18 Other US$; currency year NR

Payer NA Actual costs of tests, procedures, and visits encountered by enrolled participants; repetitive procedures and visits considered unnecessary and expendable; others considered nonexpendable

NA

Dillon (2018)17 Other : Cost simulation

2016 AU$ Payer NA WES AU$ 2,000; comparison gene panels cost NR NA


Cost analysis 2016, CAD$ Payer NA Costs of clinical and laboratory staff labor, infrastructure, WES laboratory, and bioinformatics

NA

Ewans (2018)21 Cost-benefit analysis

2016, reported in US$

Payer NA Only n=14 patients in this analysis (all with intellectual disability) Costs for diagnostic encounters and procedures recorded in the medical record (determined by using local salary data to estimate staff time, procedure; investigation costs from the Australian Medicare Benefits Schedule. Cost of single gene and Sanger sequencing, deletion/duplication studies, and biochemical tests were obtained from referral labs, WES costs were obtained from local labs

NA

Howell (2018)28 Cost-benefit analysis

2016; reported in US$ (converted from AU$)

Payer NA Calculated from data from the Australian Medicare Benefits Schedule, Royal Children's Hospital Decision Support Unit, Victorian Clinical Genetics Service, and State Neuropathology Service. Only diagnostic costs and costs related to diagnosis (e.g., anesthesia, operating room costs, ward/nursing costs, drugs related to sedation for testing, etc.) were considered, the cost of each test within each tier was aggregated for a total tier cost. Reported cost of WES gene panel: $1,639

Number of diagnoses

Monroe (2016)80 Cost analysis 2014; USD (converted from Euros)

Payer NA Reimbursement prices from Dutch Healthcare Authority were used for medical interventions, imaging and diagnostics, biochemical analysis, and surgeries; inpatient days, health professional visits, day admissions, blood products WES costs estimated at $3,972 per trio and includes cost of blood draw, DNA isolation, sample preparation, exome enrichment, sequencing, interpretation, reporting of results, data storage, and infrastructure In comparative analysis, WES replaces all genetic costs except CMA and SNP and all metabolic assessments. Also,

NA


Final




Perspective Used



assumed a scenario where WES testing would result in 50% reduction in health care utilization related to additional testing

Nolan (2016)24 Cost analysis U.S. $, year NR Payer NA Costs for initial and secondary genetic and metabolic tests estimated from data from private laboratories and included karyotype, chromosomal microarray, fragile X, methylation PCR, urine organic acids, plasma amino acids, acylcarnitine profile and lactate, single-gene tests or gene panels Source of cost of WES not explicitly reported but presumed to be from the two laboratories that conducted the diagnostic WES testing

NA

Palmer (2018)27 Cost-benefit analysis

Australian $, Year NR

Payer NA Actual costs of diagnostic tests of the patients enrolled based on case files, pathology databases, public hospital, and commercial costs Costs included billed cost of the test, courier costs, workforce costs associated with diagnostic procedures, patient admission for diagnostic tests, costs of specialist consultations, costs associated with functional testing to assess the pathogenicity of novel findings Costs of WES included cost of DNA extraction, costs related to sequencing, and costs of medical genomicist to prioritize variants and a genetic pathologist to assess pathogenicity and compile a report

Additional diagnosis

Schofield (2017)26

Cost-benefit analysis

2016, Australian $

Payer NA Traditional pathway: Cost of all diagnostic investigations and procedures, Sanger sequencing of candidate genes in DNA extracted from biopsy specimens, confirmation in parents Neuromuscular gene panel: costs of traditional pathway, cost of commercially available 464 neuromuscular gene panel, cost of confirmation with Sanger sequencing in proband and parents WES: cost of traditional pathway, cost of Sanger sequencing confirmation, cost of singleton WES, cost of trio WES

NA

Soden (2014)25 Cost analysis NR Payer NA Total costs of prior negative diagnostic testing for children who received a diagnosis, including laboratory tests, radiologic procedures, electromyograms, nerve conduction velocity studies Not considered: tests performed at outside institutions, tests necessary for patient management (e.g.

NA


Final




Perspective Used



EEG), physician visits, phlebotomy, other health care charges

Stark (2016, 2017, 2019)13-16

Cost-benefit analysis, Cost-effectiveness analysis

2015, Australian $

Payer 18 months; 20 years; 5%

Medications, imaging, pathology, biochemical testing, genetic tests, specialty medical and genetic consultations, hospital admissions, reproductive counseling or services or testing in family members. Costs obtained from hospital, state government, and testing laboratories. General patient care costs were not included

In initial publication, clinicians determined QALY’s gained based on their prognosis of disease progress in the absence of a diagnosis and thus no changes in management For reproductive outcomes, utility was estimated based on health condition at the time of birth of subsequent pregnancies and parents were assumed to benefit from an additional 0.07 QALY each as result of the birth In follow-up publication, published population norm utility values were used

Stark (2018)22 Cost-benefit analysis

Year NR, AU$ Payer NA Authors extracted all diagnostic investigations, procedures, and assessments from the medical record; costs of those were obtained from the hospital, state government medical benefits schedule, and testing laboratories

NA

Tan (2017)19 Cost-benefit analysis

2015, US$ Payer NA All costs from initial presentation to tertiary services for diagnostic assessments, first clinical genetic assessment and WES testing, all diagnostic inpatient and outpatient episodes of care including investigations, specialists consulted, duration of admission, and travel, costs of case conferences, costs incurred to the health system for travel from home

Additional diagnoses

Tsiplova (2017)79 Other: Cost-consequence analysis

2015, CAD$ Payer Costs were estimated in each year of a 5-year program, 3% discount rate

Labor (specimen prep, DNA extraction, library preparation, microarray processing, sequencing, analysis, including bioinformatics for WES, clinical interpretation, reporting), supplies (sample handling, library preparation kits, sequencing reagents, scanner consumables), follow-up testing (qPCR,, FISH, Sanger sequencing, bioinformatics computation use, small equipment, large equipment (service contracts)

NR

Vissers (2017)20 Cost-benefit analysis

2016, Euro Payer NA Actual costs of diagnostic tests performed both prior to and after inclusion in the study, unit cost prices from the Dutch

NA


Final




Perspective Used



Healthcare Authority and cover the cost of the test, interpretation of results, and physician fee. Cost of singleton WES was E1800 and trio WES E3,500 Assumed that when WES resulted in a conclusive diagnosis that tests performed in the standard pathway could have been precluded. When WES did not result in conclusive diagnosis, assumed costs would be identical except that the costs associated with genetic testing would be replaced by costs of diagnostic WES. In the WES-first pathway, assumed that once a conclusive diagnosis was reached, no additional tests would be performed

Vrijenhoek (2018)77

Other: Cost of Illness

Year NR; Euro Payer NA Costs on all healthcare activities performed at the university medical center as indicated in the patient's medical records and hospital information systems, starting with the first visit to the university medical center. All health care activities were linked to their unit costs derived from price lists issued by the Dutch Healthcare Authority Costs for WES were excluded except for WES-first strategies

NA

Walsh (2017)78 Cost-benefit analysis

Year NR; Australian $

Payer NA Costs for all investigations, diagnostic procedures, first three neurology appointments for pediatric participants, first appointment with neurologist for adult patients (these visits were considered for diagnostic purposes)

NA

Abbreviations: AU = Australian; CAD = Canadian; CI = confidence interval; CMA = chromosomal microarray; E = Euro; NA = not applicable; NR = not reported; PCR = Polymerase Chain

Reaction; QALY = quality-adjusted life year; SNP = single-nucleotide polymorphisms; U.S. = United States.


Final


Table C-6. Findings from Studies Reporting Cost Outcomes

Author (Year)

Risk of bias Cost of WES Cost per patient Cost per diagnosis

Cost per additional diagnosis

Cost-utility or cost-effectiveness

Other cost outcomes

Cordoba (2018)18

High $1,000 Cost of expendable diagnostic workup: $1,646 (95% CI, 1,439 to 1,835)

NR NR NR NR

Dillon (2018)17 High AU$ 2,000 NR In 26% of WES-diagnosed children for whom a comparator panel would have been diagnostic, the least costly panel had a higher price than the price of WES in this study

NR NR NR


Some Singleton; CAD$ 2,576 Trio; CAD$ 6,437

Cost per patient Last resort trio WES after clinical genomics consultation: CAD$ 6,138 Last resort singleton WES: CAD$ 5,125 Last resort trio WES without clinical genomics consultation: CAD$ 5,263

Cost per diagnosis Trio WES after clinical genomics consultation: CAD$ 14,405 Singleton WES: CAD$ 18,223 Trio WES without clinical genomics consultation: CAD$ 15,495

NR NR NR

Ewans (2018)21

Some Singleton; $ 1,200 Trio; $3,150

Mean cost per patient Traditional pathway: $6,742 (95% CI, $5,262 to $8,432) WES at initial symptoms presentation: $6,574 (95% CI, $4,831 to $8,524) WES at clinical genetics review: $6,918 (95% CI, 5,358 to $8,763) WES at initial symptoms presentation and reanalysis at 12 months: $6,709 (95% CI, 4,937 to $8,688) WES at clinical genetics review and reanalysis at 12 months: $7,053 (95% CI, $5,458 to $8,929)

Mean cost per diagnosis Traditional pathway: $0 (no diagnoses made) WES at initial symptoms presentation: $23,010 (95% CI, $10,135 to $102,147) (4 diagnoses) WES at clinical genetics review: $24,215 ($11,195 to $103,173) (4 diagnoses) WES at initial symptoms presentation and reanalysis at 12 months: $15,653 (95% CI, $7,619 to $49,752) (6 total diagnoses) WES at clinical genetics review and reanalysis at 12 months: $16,457 (95% CI, 8521 to $50,531) (6 total diagnoses)

Cost per additional diagnosis compared to traditional pathway WES at initial symptoms presentation: $-586 (95% CI, $-3769 to $16,144) WES at clinical genetics review: $618 (95% CI, $-2,431 to $17,439) WES at initial symptoms presentation and reanalysis at 12 months: $-77 (95%CI, $-2,990 to $7,334) WES at clinical genetics review and reanalysis at 12 months: $726 (95% CI, $-1,873 to $8,060)

NA NR


Final


Author (Year)




Other cost outcomes

Howell (2018)28

Some Commercial WES gene pane; $1,639



Compared to Path 1 Path 2: $8,559 Path 3: $3,250 Path 4: $3,650 Path 5: $1,775 Path 6: Dominates (i.e., identified more diagnoses at lower cost ) Path 7: Dominates (i.e., identifies more diagnoses at lower cost ) Sensitivity analysis varied diagnostic yield of WES and cost of WES; Path 5 also dominated under assumptions of somewhat higher diagnostic yield and 20% lower WES costs

NR NR

Monroe (2016)80

Some Trio; $3,972 Median (range) cost per patient Traditional diagnostic pathway: $14,153 ($6,343 to $47,841) Median cost savings from early WES Diagnosed participants: $5,342 ($0 to $10,684) Undiagnosed participants: $4,854 ($890 to $18,696) Cost savings from early WES leading to 50% reduction in number and cost of diagnostic trajectory Diagnosed participants: $1,660

NR NR NR NR


Final


Author (Year)




Other cost outcomes

Undiagnosed participants: $4,269

Nolan (2016)24 High Range $2,000 to $15,000

Average cost of initial and secondary genetic and metabolic testing prior to WES: $4,853 Cost of WES testing: range $2,000 to $15,000 If WES was performed after initial but prior to secondary testing, estimated average savings of $2,968

NR NR NR NR

Palmer (2018)27

Some Trio; AU$4,036 to AU $12,362 (varied by commercial lab)

Standard path: AU$11,827 (95% CI, $10,677 to $13,027) In-house Exome path: AU$9,536 (95% CI, $9,412 to $9,683)

Standard path: AU$182,243 (95% CI, $72,703 to $406,142) In-house exome path: AU$ 19,074 (95% CI, $14,421 to $27,969)

AU$-5,236 (95% CI, $2,483 to $-9,784) [Exome path was cost saving relative to standard path] Sensitivity analyses using costs of four commercial trio WES platforms demonstrated the exome path provided additional diagnoses at less costs >95% of the time for three of the four platforms. The fourth platform (which was the most expensive) resulted in an additional cost per diagnosis of $13,113 (95% CI, $8,610 to $23,728)

NR NR

Schofield (2017)26

Some Singleton; AU$1,718 Mean cost per patient (95% CI) Traditional pathway: AU$ 10,491 (AU$ 9,115 to

Mean cost per diagnosis (95% CI) Traditional pathway: AU$22,596 (AU$17,004 to

Cost per additional diagnosis compared to the traditional pathway Neuromuscular gene

NA NR


Final


Author (Year)




Other cost outcomes

AU$11,848) Neuromuscular gene pathway: AU$3,808 (AU$3,293 to AU$4,373) WES pathway: AU$6,077 (AU$ 5,284 to AU$6,846)

AU$31,498) Neuromuscular gene pathway: AU$5,077 (AU$4,228 to AU$6,100) WES pathway: AU$7,734 (AU$6,166 to AU$9,696)

pathway: AU$-23,390 (AU$-14,595 to AU$-41,184) WES pathway: AU$-13,732 (AU$-7,938 to AU$-473)

Soden (2014)25

High NR NR At an average cost of prior testing of $19,100 (range $3,248 to $55,321) per family, authors estimate that WES would be cost-effective at a cost of $2,996 per individual ($7,640 per trio)

NR NR NR

Stark (2016, 2017, 2019)13-

16

High Singleton; AU$ 1,500 to $3,100

Mean cost per patient (95% CI) Standard clinical pathway: AU$ 4,734 (AU$3,693 to AU$ 5,895) WES after basic and complex investigations: AU$ 8,384 (AU$ 7,079 to AU$ 9,619) WES after basic investigations: AU$ 5,914 (AU$ 5,243 to AU$ 6,641) WES as first-tier test: AU$3,752 (AU$ 3,752 to AU$ 3,752) For those with noninformative initial testing: WES reanalysis at 18 months: AU$ 391 (95% CI, AU$ 360 to AU$ 433) WES reanalysis every 6 months: AU$ 1,031 (AU$ 988 to AU$ 1,071) No reanalysis: AU$ 537

Mean cost per diagnosis (95% CI) Standard clinical pathway: AU$ 27,050 (AU$ 15,366 to AU$ 68,530) WES after basic and complex investigations: AU$13,415 (AU$ 10,165 to AU$ 18,351) WES after basic investigations: AU$ 9,462 (AU$ 7,497 to AU$ 12,619) WES as first-tier test: AU$ 6,003 (AU$4,841 to AU$ 7,899) For those with noninformative initial testing: WES reanalysis at 18 months: AU$ 2,838 (95% CI, 1,569 to 10,450) WES reanalysis every 6 months: AU$ 7,475 (95% CI, 3,625 to 30,400) No reanalysis: NA

Incremental cost per additional diagnosis compared to standard pathway: WES after basic and complex investigations: AU$ 8,112(AU$ 5,851 to AU$ 11,967) WES after basic investigations: AU$ 2,622 (AU$ 847 to AU$ 4,459) WES as first-tier test: AU$ -2,182 (AU$ -5,855 to AU$ 130) In 97% of simulations, WES as first-tier test was dominant (less cost with more diagnoses compared to standard care). Compared to no reanalysis: WES reanalysis at 18 months: AU$ -1,059 (95% CI, AU$ -10,502 to AU$ 1,937)

Results from 2017 publication:13 Compared to standard care after a median follow-up of 473 days: Diagnosis with WES and resulting changes in management for proband only: Cost per QALY gained AU$ -1,578 (95% CI, AU$ -205,450 to AU$ 19,780). In simulations to assess uncertainty of findings, 48.5% of simulations demonstrated cost savings from diagnosis and changes in management. Diagnosis with WES with resulting changes in management, cascade testing, and reproductive planning in first-degree relatives:

NR


Final


Author (Year)




Other cost outcomes

(95% CI, AU$ 159 to AU$ 1,051)

WES reanalysis every 6 months: AU$ 3,578 (95% CI, AU$ -232 to AU$ 17,003)

Cost per QALY gained AU$ 8,119 (95% CI, AU$ 1,962 to AU$ 38,944). In simulations to assess uncertainty of findings, 97.8% of simulations demonstrated additional costs from diagnosis and changes in management and additional family member testing and counseling. Results from 2019 publication projecting health outcomes over 20 years compared to standard care:15 WES after basic investigations: cost per QALY gained AU$ 31,144 (probands only); AU$ 20,840 (probands plus cascade outcomes in 1st degree relatives); AU$ 14,235 (probands, cascade outcomes in 1st degree relatives, reproductive outcomes)

Stark (2018)22 Some NR Usual care + conventional sequencing costs (no WES): AU$ 4,734 Standard WES: AU$ 6,777 Rapid WES: AU$ 7,029

Usual care + conventional sequencing costs (no WES): AU$27,050 (95% CI, AU$15,366 to AU$68, 530) Standard WES: AU$ 10,843 (95% CI, AU$7,488 to AU$14,090) Rapid WES: AU$ 13,388

NR NR NR


Final


Author (Year)




Other cost outcomes

(95% CI, AU$9,269 to AU$17,507)

Tan (2017)19 Some Singleton; < AU$ 2,300

Mean cost per patient (95% CI) Standard pathway no WES: $7,515 ($5,743 to $9,486) Standard pathway with WES: $9,800 ($8,033 to $11,758) WES at first genetics appointment: $5,349 ($4,583 to $6,295) WES at first tertiary presentation: $3,927 ($3,520 to $4,413)

Mean cost per diagnosis (95% CI) Standard pathway no WES: NA (study design assumed no diagnoses made) Standard pathway with WES: $18,762 ($13,640 to $26,628) WES at first genetics appointment: $10,239 ($7,667 to $14,614) WES at first tertiary presentation: $7,534 ($5,832 to $10,494)

Mean cost per additional diagnosis (95% CI) Compared to standard pathway: Standard pathway with WES: $4,804 ($3,904 to $6,523) WES at first genetics appointment: $-3,709 ($-7,491 to $ -694) WES at first tertiary presentation: $-6,412 ($-11,192 to $-2,887)

NR NR

Tsiplova (2017)79

Some CAD$ 1,655 (CAD$ 1,611 to CAD$ 1,699)

Cost per sample (95% CI) CMA: $CAD 744 (CAD$ 714 to CAD$ 773) CMA + WES: CAD$1,655 (CAD$ 1,611 to CAD$ 1,699)

NR Incremental sample cost per diagnosis of CMA +WES compared to CMA alone: CAD$ 25,458

NR NR

Vissers (2017)20

Some € 3,240 Mean costs (95% CI) per patient Standard pathway: E10,685 (9,544 to 11,909) WES pathway: E9,941* WES-first pathway: E8,356 (E7,591 to E9,247)

NR NR NR NR

Vrijenhoek (2018)77

High Trio; € 3,600 Average health care cost before WES : E16,346 Average health care costs before and including WES as last test in diagnostic trajectory: E19,946 (median E8,734, range E0 to E316,860) Costs after receiving WES as last test in diagnostic

NR NR NR Average health care cost before WES : E16,346 Average health care costs before and including WES as last test in diagnostic trajectory: E19,946 (median E8,734, range E0 to


Final


Author (Year)




Other cost outcomes

trajectory were 82% lower than healthcare costs before WES testing. Costs after receiving WES as first-tier test (i.e., no other genetic test performed) 58% lower than before WES testing

E316,860) Costs after receiving WES as last test in diagnostic trajectory were 82% lower than healthcare costs before WES testing Costs after receiving WES as first-tier test (i.e. no other genetic test performed) were 58% lower than costs before WES testing

Walsh (2017)78

High Singleton; AU$2,000 Mean cost per patient on standard investigations prior to WES: AU$ 4,013 (SD $2,761) Mean cost per patient of standard investigations and WES: AU$ 6,344 (SD NR)

Mean cost per diagnosis: Standard investigations and WES as last resort strategy: AU$ 16,027 Early WES in hypothetical scenario: AU$ 12,413

Mean cost per additional diagnosis compared to standard investigations: WES as last resort strategy: AU$ 5,889 Early WES in hypothetical scenario: AU$ 2,276

NA NR

Abbreviations: AU = Australian; CAD = Canadian; CI; confidence interval; E = Euro; NA = not applicable; NR = not reported; QALY = quality-adjusted life year; SD = standard deviation; U.S. =

United States; * = calculated value.


Final

Whole exome sequencing: final evidence report Page D-1

Appendix D. Excluded Articles

List of Exclusion Codes

X1: Ineligible publication type or study

design

X2: Ineligible population

X3: Ineligible test

X4: Ineligible outcome

X5: Non-English full text

X6: Systematic reviews for hand search

X7: Study protocol or in progress

X8: Duplicate or superseded

X9: Ineligible country

X10: Not retrievable

1. NCGENES: North Carolina clinical

genomic evaluation by nextgen exome

sequencing.

Https://clinicaltrials.gov/show/nct01969370

https://www.cochranelibrary.com/central/do

i/10.1002/central/CN-01581945/full.

Published 2013. Exclude: X8

2. Genomic sequencing in acutely ill neonates.





3. Whole-genome sequencing can improve

care of severely ill infants: study finds

technique yields high rate of diagnoses, aids

decision making related to treatment. Am J

Med Genet A. 2015;167a(8):vi-vii. PMID:

26204861. doi: 10.1002/ajmg.a.37241.

Exclude: X1

4. Comprehensive gene panels provide

advantages over clinical exome sequencing

for Mendelian diseases. Genome Biol.

2015;16:134. PMID: 26112015. doi:

10.1186/s13059-015-0693-2. Exclude: X4

5. Enhancing genomic laboratory reports to

enhance communication and empower

patients.





6. Whole-exome sequencing strategy proposed

as first-line test: WES for well-phenotyped

infants leads to high diagnostic yield. Am J

Med Genet A. 2016;170(6):1387-1388.

PMID: 27191527. doi:

10.1002/ajmg.a.37317. Exclude: X1

7. Patients express satisfaction, understanding

of whole-genome sequencing: in primary

care and cardiology, patients were generally

satisfied with their physicians'

communication of WGS results, but

expectations about its clinical benefits were

not met. Am J Med Genet A.

2018;176(4):754-755. PMID: 29575633.

doi: 10.1002/ajmg.a.38669. Exclude: X1

8. Abela L, Steindl K, Simmons L, et al. A

combined metabolic-genetic approach to

early-onset epileptic encephalopathies:

results from a Swiss study cohort.

Neuropediatrics. 2016;47. PMID. doi:

10.1055/s-0036-1583731. Exclude: X1

9. Ackerman JP, Bartos DC, Kapplinger JD,

Tester DJ, Delisle BP, Ackerman MJ. The

promise and peril of precision medicine:

phenotyping still matters most. Mayo Clin

Proc. 2016. PMID: 27810088. doi:

10.1016/j.mayocp.2016.08.008. Exclude:

X1

10. Adam S, Friedman JM. Controversy and

debate on clinical genomics sequencing-

paper 2: clinical genome-wide sequencing:

don't throw out the baby with the bathwater!

J Clin Epidemiol. 2017;92:7-10. PMID:

28916491. doi:

0.1016/j.jclinepi.2017.08.020. Exclude: X1

11. Alfares A, Alfadhel M, Wani T, et al. A

multicenter clinical exome study in

unselected cohorts from a consanguineous


Final


population of Saudi Arabia demonstrated a

high diagnostic yield. Mol Genet Metab.

2017;121(2):91-95. PMID: 28454995. doi:

10.1016/j.ymgme.2017.04.002. Exclude:

X4

12. Alfares A, Aloraini T, Subaie LA, et al.

Whole-genome sequencing offers additional

but limited clinical utility compared with

reanalysis of whole-exome sequencing.

Genet Med. 2018;20(11):1328-1333. PMID:

29565419. doi: 10.1038/gim.2018.41.

Exclude: X4

13. Al-Murshedi F, Meftah D, Scott P.

Underdiagnoses resulting from variant

misinterpretation: time for systematic

reanalysis of whole exome data? Eur J Med

Genet. 2019;62(1):39-43. PMID: 29709712.

doi: 10.1016/j.ejmg.2018.04.016. Exclude:

X1

14. Al-Nabhani M, Al-Rashdi S, Al-Murshedi

F, et al. Reanalysis of exome sequencing

data of intellectual disability samples: yields

and benefits. Clin Genet. 2018;94(6):495-

501. PMID: 30125339. doi:

10.1111/cge.13438. Exclude: X9

15. Al-Shamsi A, Hertecant JL, Souid AK, Al-

Jasmi FA. Whole exome sequencing

diagnosis of inborn errors of metabolism

and other disorders in United Arab

Emirates. Orphanet J Rare Dis.

2016;11(1):94. PMID: 27391121. doi:

10.1186/s13023-016-0474-3. Exclude: X4

16. Alsultan A, Al-Suliman AM, Aleem A,

AlGahtani FH, Alfadhel M. Utilizing

whole-exome sequencing to characterize the

phenotypic variability of sickle cell disease.

Genet Test Mol Biomarkers.

2018;22(9):561-567. PMID: 30183354. doi:

10.1089/gtmb.2018.0058. Exclude: X10

17. Ammann S, Lehmberg K, Zur Stadt U, et al.

Effective immunological guidance of

genetic analyses including exome

sequencing in patients evaluated for

hemophagocytic lymphohistiocytosis. J Clin

Immunol. 2017;37(8):770-780. PMID:

28936583. doi: 10.1007/s10875-017-0443-

1. Exclude: X4

18. Anderson JH, Tester DJ, Will ML,

Ackerman MJ. Whole-exome molecular

autopsy after exertion-related sudden

unexplained death in the young. Circ

Cardiovasc Genet. 2016;9(3):259-265.

PMID: 27114410. doi:

10.1161/circgenetics.115.001370. Exclude:

X4

19. Angione K, Eschbach K, Smith G, Joshi C,

Demarest S. Genetic testing in a cohort of

patients with potential epilepsy with

myoclonic-atonic seizures. Epilepsy Res.

2019;150:70-77. PMID: 30660939. doi:

10.1016/j.eplepsyres.2019.01.008. Exclude:

X4

20. Asan, Xu Y, Jiang H, et al. Comprehensive

comparison of three commercial human

whole-exome capture platforms. Genome

Biol. 2011;12(9):R95. PMID: 21955857.

doi: 10.1186/gb-2011-12-9-r95. Exclude:

X9

21. Assistance Publique Hopitaux De Marseille.

New diagnostic strategy in hypertrophic

cardiomyopathy.

https://ClinicalTrials.gov/show/NCT025208

56. Published 2015. Updated July. Exclude:

X7

22. Atwal PS, Brennan ML, Cox R, et al.

Clinical whole-exome sequencing: are we

there yet? Genet Med. 2014;16(9):717-719.

PMID: 24525916. doi:

10.1038/gim.2014.10. Exclude: X1

23. Ayuso C, Millan JM, Dal-Re R.

Management and return of incidental

genomic findings in clinical trials.

Pharmacogenomics J. 2015;15(1):1-5.

PMID: 25348616. doi: 10.1038/tpj.2014.62.

Exclude: X1

24. Bacchelli C, Williams HJ. Opportunities

and technical challenges in next-generation

sequencing for diagnosis of rare pediatric

diseases. Expert Rev Mol Diagn.

2016;16(10):1073-1082. PMID: 27560481.

doi: 10.1080/14737159.2016.1222906.

Exclude: X1

25. Bademci G, Diaz-Horta O, Guo S, et al.

Identification of copy number variants


Final


through whole-exome sequencing in

autosomal recessive nonsyndromic hearing

loss. Genet Test Mol Biomarkers.

2014;18(9):658-661. PMID: 25062256. doi:

10.1089/gtmb.2014.0121. Exclude: X4

26. Bagnall RD, Das KJ, Duflou J, Semsarian

C. Exome analysis-based molecular autopsy

in cases of sudden unexplained death in the

young. Heart Rhythm. 2014;11(4):655-662.

PMID: 24440382. doi:

10.1016/j.hrthm.2014.01.017. Exclude: X2

27. Bahamat AA, Assidi M, Lary SA, et al. Use

of array comparative genomic hybridization

for the diagnosis of DiGeorge Syndrome in

Saudi Arabian population. Cytogenet

Genome Res. 2018;154(1):20-29. PMID:

29455205. doi: 10.1159/000487094.

Exclude: X3

28. Bainbridge MN, Wiszniewski W, Murdock

DR, et al. Whole-genome sequencing for

optimized patient management. Sci Transl

Med. 2011;3(87):87re83. PMID: 21677200.

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Exclude: X1

29. Baker M. Increasing precision in medicine -

tackling the bottleneck of variant

interpretation. Drugs Today (Barc).

2016;52(7):395-398. PMID: 27540598. doi:

10.1358/dot.2016.52.7.2533694. Exclude:

X1

30. Balci TB, Hartley T, Xi Y, et al. Debunking

Occam's razor: diagnosing multiple genetic

diseases in families by whole-exome

sequencing. Clin Genet. 2017;92(3):281-

289. PMID: 28170084. doi:

10.1111/cge.12987. Exclude: X4

31. Bardakjian TM, Helbig I, Quinn C, et al.

Genetic test utilization and diagnostic yield

in adult patients with neurological

disorders. Neurogenetics. 2018;19(2):105-

110. PMID: 29589152. doi:

10.1007/s10048-018-0544-x. Exclude: X4

32. Bartholdi D, Miny P. [Genetic testing in the

fetus and child]. Ther Umsch.

2013;70(11):621-631. PMID: 24168795.

doi: 10.1024/0040-5930/a000457. Exclude:

X5

33. Basel D, McCarrier J. Ending a diagnostic

odyssey: family education, counseling, and

response to eventual diagnosis. Pediatr Clin

North Am. 2017;64(1):265-272. PMID:

27894449. doi: 10.1016/j.pcl.2016.08.017.

Exclude: X1

34. Basha M, Demeer B, Revencu N, et al.

Whole exome sequencing identifies

mutations in 10% of patients with familial

non-syndromic cleft lip and/or palate in

genes mutated in well-known syndromes. J

Med Genet. 2018;55(7):449-458. PMID:

29500247. doi: 10.1136/jmedgenet-2017-

105110. Exclude: X1

35. Bauer P, Kandaswamy KK, Weiss MER, et

al. Development of an evidence-based

algorithm that optimizes sensitivity and

specificity in ES-based diagnostics of a

clinically heterogeneous patient population.

Genet Med. 2019;21(1):53-61. PMID:

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6. Exclude: X4

36. Beale S, Sanderson D, Sanniti A, Dundar Y,

Boland A. A scoping study to explore the

cost-effectiveness of next-generation

sequencing compared with traditional

genetic testing for the diagnosis of learning

disabilities in children. Health Technol

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26132578. doi: 10.3310/hta19460. Exclude:

X6

37. Becherucci F, Mazzinghi B, Landini S, et

al. Whole-exome sequencing for

personalized managementof idiopathic

nephrotic syndrome. Nephrology Dialysis

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10.1093/ndt/gfy104.FO057. Exclude: X1

38. Beheshtian M, Saee Rad S, Babanejad M, et

al. Impact of whole exome sequencing

among Iranian patients with autosomal

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Med. 2015;18(11):776-785. PMID:

26497376. doi: 0151811/aim.009. Exclude:

X9

39. Bell SG. Ethical implications of rapid

whole-genome sequencing in neonates.

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Final


29436358. doi: 10.1891/0730-0832.37.1.42.

Exclude: X1

40. Bergant G, Maver A, Lovrecic L, Cuturilo

G, Hodzic A, Peterlin B. Comprehensive

use of extended exome analysis improves

diagnostic yield in rare disease: a

retrospective survey in 1,059 cases. Genet

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41. Bernardini L, Alesi V, Loddo S, et al. High-

resolution SNP arrays in mental retardation

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42. Bettencourt C, Lopez-Sendon JL, Garcia-

Caldentey J, et al. Exome sequencing is a

useful diagnostic tool for complicated forms

of hereditary spastic paraplegia. Clin Genet.

2014;85(2):154-158. PMID: 23438842. doi:

10.1111/cge.12133. Exclude: X1

43. Biesecker LG, Nussbaum RL, Rehm HL.

Distinguishing variant pathogenicity from

genetic diagnosis: how to know whether a

variant causes a condition. JAMA.

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44. Bittles AH. Genetics and global healthcare.

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45. Bochud M, Currat C, Chapatte L, Roth C,

Mooser V. High participation rate among 25

721 patients with broad age range in a

hospital-based research project involving

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10.4414/smw.2017.14528. Exclude: X2

46. Bodian DL, Klein E, Iyer RK, et al. Utility

of whole-genome sequencing for detection

of newborn screening disorders in a

population cohort of 1,696 neonates. Genet

Med. 2016;18(3):221-230. PMID:

26334177. doi: 10.1038/gim.2015.111.

Exclude: X2

47. Borghesi A, Mencarelli MA, Memo L, et al.

Intersociety policy statement on the use of

whole-exome sequencing in the critically ill

newborn infant. Ital J Pediatr.

2017;43(1):100. PMID: 29100554. doi:

10.1186/s13052-017-0418-0. Exclude: X1

48. Bourgeron T. Current knowledge on the

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10.1016/j.crvi.2016.05.004. Exclude: X1

49. Bowdin S, Ray PN, Cohn RD, Meyn MS.

The genome clinic: a multidisciplinary

approach to assessing the opportunities and

challenges of integrating genomic analysis

into clinical care. Hum Mutat.

2014;35(5):513-519. PMID: 24599881. doi:

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50. Bowdin SC, Hayeems RZ, Monfared N,

Cohn RD, Meyn MS. The SickKids

Genome Clinic: developing and evaluating

a pediatric model for individualized

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2016;89(1):10-19. PMID: 25813238. doi:

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51. Brett GR, Wilkins EJ, Creed ET, et al.

Genetic counseling in the era of genomics:

what's all the fuss about? J Genet Couns.

2018;27(5):1010-1021. PMID: 29368275.

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52. Brunham LR, Hayden MR. Medicine.

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53. Bujakowska KM, Fernandez-Godino R,

Place E, et al. Copy-number variation is an

important contributor to the genetic

causality of inherited retinal degenerations.

Genet Med. 2017;19(6):643-651. PMID:

27735924. doi: 10.1038/gim.2016.158.

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Final


54. Cai Y, Huang T. Accelerating precision

medicine through genetic and genomic big

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55. Centre Hospitalier Universitaire de

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08. Published 2017. Updated September 20.

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56. Centre Hospitalier Universitaire Dijon.

Evaluate and understand preferences and

representations in families of patients with

regard to high-throughput sequencing

technology for diagnostic purposes.

Published. Exclude: X10


Evaluation of the diagnostic contribution of

high-throughput exome sequencing for

patients with convulsive encephalopathy of

unknown etiology: pilot study to improve

genetic counselling.


46. Published 2013. Updated September.

Exclude: X7


Screening for genes in patients with

congenital neutropenia.



Exclude: X8


Medico-economic evaluation of different

high-throughput sequencing strategies in the

diagnosis of patients with intellectual

deficiency.


06. Published 2017. Updated June 28.

Exclude: X7


Secondary findings from high-throughput

sequencing: how to announce them with

respect to the patient's needs.


27. Published 2017. Updated November 4.

Exclude: X7


Identification of the molecular and/or

pathophysiological bases of developmental

diseases.


93. Published 2017. Updated March 13.

Exclude: X7


Contribution of high throughput RNA

sequencing combined with sequencing of

whole genomes in the diagnosis of

intellectual disability.


97. Published 2019. Updated February 4.

Exclude: X7

63. Chacon-Camacho OF, Garcia-Montano LA,

Zenteno JC. The clinical implications of

molecular monitoring and analyses of

inherited retinal diseases. Expert Rev Mol

Diagn. 2017;17(11):1009-1021. PMID:

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10.1080/14737159.2017.1384314. Exclude:

X1

64. Chan LF, Campbell DC, Novoselova TV,

Clark AJ, Metherell LA. Whole-exome

sequencing in the differential diagnosis of

primary adrenal insufficiency in children.

Front Endocrinol (Lausanne). 2015;6:113.

PMID: 26300845. doi:

10.3389/fendo.2015.00113. Exclude: X4

65. Charbit-Henrion F, Parlato M, Hanein S, et

al. Diagnostic yield of next-generation

sequencing in very early-onset

inflammatory bowel diseases: a multicenter

study. J Crohns Colitis. 2018. PMID:

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Exclude: X4

66. Charite University; German Federal

Ministry of Education Research. Mutation

exploration in non-acquired, genetic

disorders and its impact on health economy

and life quality.


29. Published 2015. Updated January 31.

Exclude: X7


Final


67. Charng WL, Karaca E, Coban Akdemir Z,

et al. Exome sequencing in mostly

consanguineous Arab families with

neurologic disease provides a high potential

molecular diagnosis rate. BMC Med

Genomics. 2016;9(1):42. PMID: 27435318.

doi: 10.1186/s12920-016-0208-3. Exclude:

X4

68. Chen Z, Wang JL, Tang BS, et al. Using

next-generation sequencing as a genetic

diagnostic tool in rare autosomal recessive

neurologic Mendelian disorders. Neurobiol

Aging. 2013;34(10):2442.e2411-2447.

PMID: 23726790. doi:

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69. Cherot E, Keren B, Dubourg C, et al. Using

medical exome sequencing to identify the

causes of neurodevelopmental disorders:

experience of 2 clinical units and 216

patients. Clin Genet. 2018;93(3):567-576.

PMID: 28708303. doi: 10.1111/cge.13102.

Exclude: X3

70. Chiara M, Pavesi G. Evaluation of quality

assessment protocols for high throughput

genome resequencing data. Front Genet.

2017;8:94. PMID: 28736571. doi:

10.3389/fgene.2017.00094. Exclude: X1

71. Children's Hospital of Fudan University.

Etiology and treatment of neonatal seizure.


41. Published 2016. Updated August 8.

Exclude: X7

72. Children's Hospital of Philadelphia.

LeukoSEQ: whole genome sequencing as a

first-line diagnostic tool for

leukodystrophies.



Exclude: X7

73. Children's Hospital of Philadelphia. The

Myelin disorders biorepository project.


69. Published 2016. Updated December 8.

Exclude: X7

74. Children's Mercy Hospital Kansas City.

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Final


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Final


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Final


233. National Human Genome Research

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351. Weck KE. Interpretation of genomic

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352. Weiss K, Kurolap A, Paperna T, et al. Rare

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353. Wenger AM, Guturu H, Bernstein JA,

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354. Willemsen MH, Kleefstra T. Making

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355. Williams JL, Rahm AK, Zallen DT, et al.

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356. Wilson BJ, Miller FA, Rousseau F.

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10.1016/j.jclinepi.2017.08.018. Exclude:

X1

357. Wiszniewski W, Gawlinski P, Gambin T, et

al. Comprehensive genomic analysis of

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X4

358. Worst BC, Van Tilburg CM,

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359. Worthey EA. Analysis and annotation of

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X1

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361. Wouters RHP, Bijlsma RM, Frederix GWJ,

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X1

362. Wouters RHP, Cornelis C, Newson AJ,

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363. Wright CF, Fitzgerald TW, Jones WD, et al.

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Final


25529582. doi: 10.1016/s0140-

6736(14)61705-0. Exclude: X4

364. Wright CF, McRae JF, Clayton S, et al.

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365. Xiao B, Qiu W, Ji X, et al. Marked yield of

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368. Yao T, Udwan K, John R, et al. Integration

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10.1007/s10048-017-0521-9. Exclude: X4


Final


Appendix E. Individual Study Risk of Bias Assessments

Table E-1. Risk of Bias Assessment-Part 1 ................................................................................. E-1



Table E-4. Quality of Health Economic Studies-Part 1 ............................................................. E-11

Table E-5. Quality of Health Economic Studies-Part 2 ............................................................. E-12

Table E-6. Quality of Health Economic Studies -Part 3 ............................................................ E-13

Table E-7. Risk of Bias Assessment-Overall Summary ............................................................ E-14


Final

Whole exome sequencing: final evidence report Page E-1

Table E-1. Risk of Bias Assessment-Part 1

Author (Year) Study Design

Was the study population described in adequate detail?

Was participant inclusion/exclusion criteria appropriate?

Could the way in which participants were selected introduce bias?

For RCTs, was the method of randomization and allocation concealment adequate and were baseline characteristics similar among groups?

For nonrandomized comparative studies, is the comparison group appropriate?

Balridge (2017)43 Single-arm observational cohort Probably Yes Probably Yes Probably No NA NA

Bourchany (2017)42 Single-arm observational cohort No Probably Yes Unclear NA NA

Cordoba (2018)18 Single-arm observational cohort Yes Yes Probably No NA NA

Daga (2018)41 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Dillon (2018)17 Modeling Study Probably No Unclear Probably No NA NA

Ding (2014)75 Modeling Study NA NA NA NA NA

Dragojlovic (2018)23 Modeling Study No Unclear Unclear NA NA

Evers (2017)52 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Ewans (2018)21 Single-arm observational cohort Probably No Unclear Unclear NA NA

Hamilton (2016)101 Single-arm observational cohort No No Yes NA Yes

Hauer (2017)51 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Howell (2018)28 Single-arm observational cohort plus economic-modeling study Yes Yes Probably No NA NA

Iglesias (2014)46 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Jones (2018)50 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Jurgens (2015)65 Single-arm observational cohort Probably No Probably Yes Unclear NA NA

Lee (2015)73 Single-arm observational cohort Yes Yes No NA NA

Mann (2019)48 Single-arm observational cohort Yes Yes No NA NA

Matias (2019)49 Controlled (two or more groups) observational cohort Yes Yes No NA Yes

McConkie-Rosell (2018)69 Single-arm observational cohort Yes Yes Probably No NA NA

Meng (2017)40 Single-arm observational cohort Yes Yes Probably No NA NA

Monies (2017)61 Single-arm observational cohort Probably No Probably No Unclear NA NA


Final








Monroe (2016)80 Single-arm observational cohort Yes Yes Probably No NA NA

Muramatsu (2017)70 Single-arm observational cohort Yes Unclear Unclear NA NA

Niguidula (2018)39 Single-arm observational cohort Probably Yes Probably Yes Yes NA NA

Nolan (2016)24 Single-arm observational cohort Probably Yes Probably Yes Unclear NA NA

Palmer (2018)27 Single-arm observational cohort Yes Yes Probably No NA NA

Perucca (2017)34 Single-arm observational cohort Yes Yes No NA NA

Posey (2015)63 Single-arm observational cohort Probably Yes Yes Probably No NA NA

Ream (2014)58 Single-arm observational cohort Yes Yes No NA NA

Retterer (2016)64 Single-arm observational cohort Yes Yes No NA NA

Roche (2019)76 Single-arm observational cohort Probably Yes Yes Probably No NA NA

Sawyer (2016)55 Single-arm observational cohort Yes Yes No NA NA

Schofield (2017)26 Controlled (two or more groups) observational cohort Probably Yes Probably Yes Unclear NA Probably Yes

Shamriz (2016)53 Case series Yes Yes Yes NA NA

Shashi (2015)74 Single-arm observational cohort Yes Yes No NA NA

Snoeijen-Schouwenaars (2019)32 Single-arm observational cohort Yes Yes Probably No NA NA

Soden (2014)25 Single-arm observational cohort Yes Yes Probably No NA NA

Srivastava (2014)57 Single-arm observational cohort Probably Yes Yes Probably No NA NA

Stark (2016, 2017, 2019)13-16 Single-arm observational cohort Unclear Probably Yes Unclear NA NA

Stark (2018)22 Single-arm observational cohort Probably No Unclear Unclear NA NA

Strauss (2017)60 Single-arm observational cohort Probably Yes Probably No Probably Yes NA NA

Tammimies (2015)72 Single-arm observational cohort Yes Yes No NA NA


Final








Tan (2017)19 Single-arm observational cohort Yes Yes Probably No NA NA

Tarailo-Graovac (2016)44 Single-arm observational cohort Yes Yes No NA NA

Tsiplova (2017)79 Modeling Study Probably No NA NA NA Probably Yes

Valencia (2015)54 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Vanderver (2016)71 Single-arm observational cohort Yes Yes Probably No NA NA

Vissers (2017)20 Single-arm trial Yes Yes No NA Yes

Vrijenhoek (2018)77 Single-arm observational cohort Probably No NR Unclear NA NA

Waldrop (2019)47 Single-arm observational cohort Yes Probably Yes Probably No NA NA

Walsh (2017)78 Single-arm observational cohort Probably Yes Probably Yes Probably No NA NA

Willing (2015)56 Single-arm observational cohort Yes Yes Probably No NA NA

Yang (2014)66 Single-arm observational cohort Yes Yes No NA NA

Zhu (2015)45 Single-arm observational cohort Yes Yes Probably No NA NA

Abbreviations: NA = not applicable; NR = not reported


Final



Author (Year)

For nonrandomized comparative studies, does the analysis control for important baseline differences between groups or other known confounders?

Was the test and/or testing strategy described in adequate detail?

Were there important deviations from the intended tests or testing strategies used?

Were outcome assessors blinded?

Balridge (2017)43 NA Probably Yes Unclear Unclear

Bourchany (2017)42 No Yes Probably No Unclear

Cordoba (2018)18 NA Probably Yes No Unclear

Daga (2018)41 NA Yes Probably No Unclear

Dillon (2018)17 NA No Probably No Unclear

Ding (2014)75 NA No NR NA

Dragojlovic (2018)23 NA Probably Yes Unclear Unclear

Evers (2017)52 NA Yes No No

Ewans (2018)21 NA Probably Yes Unclear Unclear

Hamilton (2016)101 Yes Yes Yes No

Hauer (2017)51 NA Probably Yes Probably No No

Howell (2018)28 NA Probably Yes Unclear Probably No

Iglesias (2014)46 NA Probably No Probably No Unclear

Jones (2018)50 NA No Unclear No

Jurgens (2015)65 NA Yes No NA

Lee (2015)73 NA Yes No No

Mann (2019)48 NA Yes No No

Matias (2019)49 Yes Yes No Probably No

McConkie-Rosell (2018)69 NA NA NA Yes


Final


Author (Year)





Meng (2017)40 NA Yes Probably No Probably No

Monies (2017)61 NA Probably Yes Probably No Unclear

Monroe (2016)80 NA Probably Yes Probably No NR

Muramatsu (2017)70 NA Yes No No

Niguidula (2018)39 NA No NR No

Nolan (2016)24 NA Probably No Unclear Unclear

Palmer (2018)27 NA Probably No Unclear Unclear

Perucca (2017)34 NA Yes No No

Posey (2015)63 NA Yes No NA

Ream (2014)58 NA Yes No No

Retterer (2016)64 NA Yes No NA

Roche (2019)76 NA No Probably No Unclear

Sawyer (2016)55 NA Yes No No

Schofield (2017)26 NR Probably No Unclear Unclear

Shamriz (2016)53 NA Yes No No

Shashi (2015)74 NA Yes No No

Snoeijen-Schouwenaars (2019)32 NA Yes No

Soden (2014)25 NA Probably No Probably No Unclear

Srivastava (2014)57 NA Yes No Probably No

Stark (2016, 2017, 2019)13-16 NA No Unclear Unclear


Final


Author (Year)





Stark (2018)22 Probably No Yes Probably No Unclear

Strauss (2017)60 NA Yes Probably No Unclear

Tammimies (2015)72 NA Yes No No

Tan (2017)19 NA Probably No Probably No Unclear

Tarailo-Graovac (2016)44 NA Yes No No

Tsiplova (2017)79 Probably Yes Probably Yes NA NA

Valencia (2015)54 NA Yes Probably No Unclear

Vanderver (2016)71 NA Yes Probably No No

Vissers (2017)20 NA Yes No No

Vrijenhoek (2018)77 NA No Probably No Probably No

Waldrop (2019)47 NA Probably No Probably No Unclear

Walsh (2017)78 NA Probably No Unclear Unclear

Willing (2015)56 NA Yes No No

Yang (2014)66 NA Yes Probably No Unclear

Zhu (2015)45 NA Yes No No



Final



Author (Year)

For clinical utility measures and analyses, are the measures and statistical methods used valid and appropriate (and similarly applied among groups for comparative studies)?

Were clinical utility outcomes data available for at least 80% of participants that were enrolled without any evidence of differential attrition?

For health outcomes and analyses, are the measures and statistical methods used valid and appropriate (and similarly applied among groups for comparative studies)?

Were health outcomes data available for at least 80% of participants that were enrolled? Any evidence of differential attrition?

For safety outcomes and analyses, are the measures and statistical methods used valid and appropriate (and similarly applied among groups for comparative studies)?

Were safety outcomes data available for at least 80% of participants that were enrolled? Any evidence of differential attrition?

Balridge (2017)43 Probably No Probably Yes NA NA Probably Yes Probably Yes

Bourchany (2017)42 NR No NA NA Probably Yes Probably Yes

Cordoba (2018)18 No Yes NA NA NA NA

Daga (2018)41 NR Probably Yes NA NA NA NA

Dillon (2018)17 NA NA NA NA NA NA

Ding (2014)75 NA NA NA NA Unclear NA

Dragojlovic (2018)23 Unclear NA NR NA NA NA

Evers (2017)52 Yes Yes NA NA NA NA

Ewans (2018)21 NA NA NA NA NA NA

Hamilton (2016)101 NA NA NA NA Probably No No

Hauer (2017)51 Unclear Yes NA NA NA NA

Howell (2018)28 No Unclear NA NA NA NA

Iglesias (2014)46 Probably Yes Probably Yes NA NA NA NA

Jones (2018)50 Probably Yes Yes Probably Yes Yes NA NA

Jurgens (2015)65 NA NA NA NA Probably Yes Yes

Lee (2015)73 NA NA NA NA Yes Yes

Mann (2019)48 No Yes NA NA NA NA


Final


Author (Year)







Matias (2019)49 Yes Yes NA NA NA NA

McConkie-Rosell (2018)69 NA NA NA NA Yes Yes

Meng (2017)40 No Probably Yes Probably No Yes Probably Yes Probably Yes

Monies (2017)61 NA NA NA NA Probably Yes Probably Yes

Monroe (2016)80 NA NA NA Unclear NA NA

Muramatsu (2017)70 NA NA NA NA Yes Yes

Niguidula (2018)39 No NA NA NA NA NA

Nolan (2016)24 Probably No Yes NA Yes Yes Yes

Palmer (2018)27 No Probably Yes NA NA NA NA

Perucca (2017)34 No Yes Probably No Unclear NA NA

Posey (2015)63 NA NA NA NA Yes Yes

Ream (2014)58 Probably Yes Yes No Probably No No No

Retterer (2016)64 NA NA NA NA Yes Yes

Roche (2019)76 NA NA NA NA Unclear Probably Yes

Sawyer (2016)55 Unclear Unclear NA NA No No

Schofield (2017)26 NA NA NA NA NA NA

Shamriz (2016)53 Probably Yes Yes Probably Yes Yes NA NA


Final


Author (Year)







Shashi (2015)74 NA NA NA NA Yes Yes

Snoeijen-Schouwenaars (2019)32 No Yes Probably Yes Yes NA NA

Soden (2014)25 No Yes NA NA NA NA

Srivastava (2014)57 No NR NA NA NA NA

Stark (2016, 2017, 2019)13-16 Probably No Yes Yes Yes Yes Yes

Stark (2018)22 Probably No Probably Yes Probably Yes Probably Yes NA NA

Strauss (2017)60 NA NA NA NA Yes Yes

Tammimies (2015)72 NA NA NA NA Yes Yes

Tan (2017)19 Probably Yes Yes NA NA NA NA

Tarailo-Graovac (2016)44 Probably No Yes NA NA Yes Yes

Tsiplova (2017)79 NA NA NA NA NA NA

Valencia (2015)54 Probably No Probably Yes NA NA Yes Probably Yes

Vanderver (2016)71 NA NA NA NA Yes Yes

Vissers (2017)20 NA NA NA NA Yes Yes

Vrijenhoek (2018)77 NA NA NA NA NA NA

Waldrop (2019)47 NR Probably Yes NA NA NA NA

Walsh (2017)78 NA NA NA NA NA NA


Final


Author (Year)







Willing (2015)56 Probably No Yes Yes Yes

Yang (2014)66 NA NA NA NA Probably Yes Yes

Zhu (2015)45 Probably No Unclear NA NA NA NA



Final


Table E-4. Quality of Health Economic Studies-Part 1

Author (Year)

Was the study objective presented in a clear, specific, and measurable manner?

Were the perspective of the analysis (societal, third-party payer, and so on) and reasons for its selection stated?

Were variable estimates used in the analysis from the best available source (i.e., Randomized Control Trial-Best, Expert Opinion-Worst)?

If estimates came from a subgroup analysis, were the groups pre-specified at the beginning of the study?

Was uncertainty handled by: (i) statistical analysis to address random events; (ii) sensitivity analysis to cover a range of assumptions?

Was incremental analysis performed between alternatives for resources and costs?

Was the methodology for data abstraction (including value health states and other benefits) stated?

Cordoba (2018)18 No Yes Unclear NA No No No

Dillon (2018)17 No Unclear Unclear NA No No No

Dragojlovic (2018)23 Yes Yes Yes NA Yes No Unclear

Ewans (2018)21 Yes Yes Yes NA Yes Yes Yes

Howell (2018)28 Yes Yes Yes NA Yes Yes Yes

Monroe (2016)80 Yes Yes Yes NA No Unclear Yes

Nolan (2016)(#6227) No No Unclear NA No NA NA

Palmer (2018)27 Yes Yes Yes NA Yes Yes Yes

Schofield (2017)26 Yes Yes Yes NA Yes Yes Unclear

Soden (2014)25 No No Unclear NA No No No

Stark (2018)22 Yes Yes Yes NA Yes No Yes

Stark (2016, 2017, 2019)13-16 Yes Yes Unclear NA Yes Yes Unclear

Tan (2017)19 Yes Yes Yes NA Yes Yes Yes

Tsiplova (2017)79 Yes Yes Yes NA Yes Yes Yes

Vissers (2017)20 Yes Yes Yes NA Yes No Yes

Vrijenhoek (2018)77 No Yes Yes NA No No Unclear

Walsh (2017)78 No Yes Unclear NA No Yes Unclear



Final


Table E-5. Quality of Health Economic Studies-Part 2

Author (Year)

Did the analytic horizon allow time for all relevant and important outcomes? Were benefits and costs that went beyond 1 year discounted (3–5%) and justification given for the discount rate?

Was the measurement of costs appropriate and the methodology for the estimation of quantities and unit costs clearly described?

Was the primary outcome measure(s) for the economic evaluation clearly stated and were the major short-term, long-term and negative outcomes included?

Were the health outcomes measures/scales valid and reliable? If previously tested valid and reliable measures were not available, was justification given for the measures/scales used?

Were the economic model (including structure), study methods and analysis, and the components of the numerator and denominator displayed in a clear transparent manner?

Were the choice of economic model, main assumptions and limitations of the study stated and justified?

Cordoba (2018)18 NA Unclear No NA No Unclear

Dillon (2018)17 NA No No NA No Unclear

Dragojlovic (2018)23 NA Yes Unclear NA Unclear No

Ewans (2018)21 NA Yes Unclear NA Yes No

Howell (2018)28 NA Yes Yes NA Yes Unclear

Monroe (2016)80 NA Yes Unclear NA Yes Yes

Nolan (2016)(#6227) Yes No No NA Yes Yes

Palmer (2018)27 NA Unclear Yes NA Yes Unclear

Schofield (2017)26 NA Yes Yes NA Yes Yes

Soden (2014)25 NA No No NA No Unclear

Stark (2018)22 NA Yes Unclear NA Yes Unclear

Stark (2016, 2017, 2019)13-16

Yes Unclear Unclear NA Yes Yes

Tan (2017)19 NA Yes No NA Yes No

Tsiplova (2017)79 Yes Yes Unclear NA Yes No

Vissers (2017)20 NA Yes Unclear NA Unclear Unclear

Vrijenhoek (2018)77 NA Yes No NA No Unclear

Walsh (2017)78 NA Yes No NA No Unclear



Final


Table E-6. Quality of Health Economic Studies -Part 3

Author (Year)

Did the author(s) explicitly discuss direction and magnitude of potential biases?

Were the conclusions/recommendations of the study justified and based on the study results?

Was there a statement disclosing the source of funding for the study? Total Score

Cordoba (2018)18 No Unclear Yes 26

Dillon (2018)17 No Unclear No 15

Dragojlovic (2018)23 Yes Unclear Yes 60

Ewans (2018)21 No Unclear Yes 73

Howell (2018)28 No Yes No 84

Monroe (2016)80 Yes Yes Yes 79

Nolan (2016)(#6227) No Unclear Yes 40

Palmer (2018)27 No Yes Yes 79

Schofield (2017)26 Unclear Yes Yes 89

Soden (2014)25 No Unclear Yes 18

Stark (2018)22 No Yes Yes 75

Stark (2016, 2017, 2019)13-16

Yes Yes Yes 80

Tan (2017)19 Unclear Unclear Yes 73

Tsiplova (2017)79 Unclear Unclear Yes 80

Vissers (2017)20 Yes Yes Yes 73

Vrijenhoek (2018)77 No Unclear Yes 31

Walsh (2017)78 No Yes Yes 44

Notes: a Based on scale of 0 (worst quality) to 100 (best quality); studies <60 were assigned high risk of bias; studies between 60

to 89 were assigned some risk of bias, and studies >=90 were assigned low risk of bias.


Final


Table E-7. Risk of Bias Assessment-Overall Summary

Author (Year) Clinical Utility Health Outcomes Safety Cost Comments

Balridge (2017)43 Some risk of bias NA Some risk of bias NA Little information about how clinical utility measures were defined and abstracted from the medical record.

Bourchany (2017)42 High risk of bias NA Low risk of bias NA No information about how clinical utility measures collected; missing information for many participants regarding clinical utility measures.

Cordoba (2018)18 Some risk of bias NA NA High risk of bias No information about how clinical utility measures were specified and ascertained; very limited information about how costs were determined and specifically designation of expendable vs. nonexpendable costs.

Daga (2018)41 Some risk of bias NA NA NA No information about how clinical utility measures were collected.

Dillon (2018)17 NA NA NA High risk of bias Very little information about specific costs used and where obtained; includes costs from different years without indexing to a specific year.

Ding (2014)75 NA NA Some risk of bias NA This was a modeling study.

Dragojlovic (2018)23 NA NA NA Some risk of bias Missing data for a reasonable proportion of participants enrolled; cost analysis not well-described.

Evers (2017)52 Some risk of bias NA NA NA None

Ewans (2018)21 NA NA NA Some risk of bias Cost analysis based only on a subcohort of 14 participants with intellectual disability.

Hamilton (2016)101 NA NA High risk of bias NA Records or sequencing was not available for a large proportion of the cohort; excluded low coverage genes from analysis.

Hauer (2017)51 High risk of bias NA NA NA None

Howell (2018)28 High risk of bias NA NA Some risk of bias No information about how clinical utility was ascertained; finding is not similar to findings reported in other studies suggesting a problem in ascertainment. With respect to cost, main assumptions and limitations not well discussed; no statement disclosing funding.

Iglesias (2014)46 Some risk of bias NA NA NA No details regarding how medical record abstraction or test was conducted.

Jones (2018)50 High risk of bias Some risk of bias NA NA None

Jurgens (2015)65 NA NA Some risk of bias NA None

Lee (2015)73 NA NA Low risk of bias NA None

Mann (2019)48 Some risk of bias NA NA NA None


Final



Matias (2019)49 Some risk of bias NA NA NA Each patient is compared to themselves pre- and post -WES, then finding are compared between positive WES group and negative WES group.


NA NA Low risk of bias NA None

Meng (2017)40 Some risk of bias Some risk of bias Some risk of bias NA None

Monies (2017)61 NA NA Some risk of bias NA Population tested was poorly described; conducted in a country known to have a higher degree of consanguinity.

Monroe (2016)80 NA NA NA Some risk of bias No sensitivity analysis or consideration of uncertainty in estimates; incremental analysis not entirely clear.

Muramatsu (2017)70 NA NA Low risk of bias NA None

Niguidula (2018)39 High risk of bias NA NA NA Clinical utility measures based on provider recall survey, no verification with medical records. Survey response rate was 2.2%, which could introduce very serious risk for selection bias.

Nolan (2016)24 Some risk of bias NA Some risk of bias High risk of bias For clinical utility and health outcomes, outcomes measured through review of medical record; unclear whether outcomes were defined a priori, outcome assessors likely not masked to testing intervention. For cost outcomes, details of costing methodology not provided, utilities established through clinician assessment of prognosis, currency year not specified, no sensitivity analyses for key parameters

Palmer (2018)27 Some risk of bias NA NA Some risk of bias Specification and method of ascertainment of clinical utility measures not well-described; unclear whether participants received all of the testing in first and second tier, or only some of the testing and the impact of this on cost analysis not discussed.

Perucca (2017)34 High risk of bias High risk of bias NA NA None

Posey (2015)63 NA NA Low risk of bias NA None

Ream (2014)58 Some risk of bias High risk of bias High risk of bias NA Study only designed to measure potential changes in therapy. These were pre-defined, reducing risk of bias.

Retterer (2016)64 NA NA Low risk of bias NA None

Roche (2019)76 NA NA High risk of bias NA Focused on evaluation of nonmedically actionable results; reporting of medically actionable results was secondary; test used was not described; the specific variants considered medically actionable were not described; the variants considered nonmedically actionable appear to have overlap with what some would consider medically actionable.


Final



Sawyer (2016)55 High risk of bias NA NA NA None

Schofield (2017)26 NA NA NA Some risk of bias Unclear what tests, out of the traditional pathway, participants in the WES or neuromuscular gene panel also received and at what time; those who got WES or NMD gene panel remained undiagnosed after traditional pathway, thus are likely not similar; several assumptions were made for cost analysis and costs of counseling in WES approach was not included, unclear whether all costs were captured from outside settings given duration of study (over 15 years).

Shamriz (2016)53 High risk of bias High risk of bias NA NA None

Shashi (2015)74 NA NA Low risk of bias NA ACMG list of medically actionable variants published during study; only 59 patients tested after publication.


High risk of bias High risk of bias NA NA None

Soden (2014)25 High risk of bias NA NA High risk of bias Ascertainment of clinical utility was partly through physician interview/recall, was not specified, and was only reported for those with diagnosis; multiple issues with cost analysis including no methods described, unknown year of currency, no incremental analysis, no sensitivity analysis, with results that are difficult to interpret.

Srivastava (2014)57 High risk of bias NA NA NA Management changes only reported for patients with diagnosis. Methods for determining management changes NR.

Stark (2016, 2017, 2019)13-16

Some risk of bias Some risk of bias NA Some risk of bias

For clinical utility and health outcomes, outcomes measured through review of medical record and unclear whether outcomes were defined a priori, outcome assessors likely not masked to testing intervention. For cost outcomes, details of costing methodology not provided, utilities established through clinician assessment of prognosis, currency year not specified, no sensitivity analyses for key parameters

Stark (2018)22 High risk of bias Some risk of bias NA Some risk of bias No information about how clinical utility measures were ascertained, and no reporting of measures for the standard WES cohort in this article. Cost analysis was not incremental, economic model and main assumptions not clear, magnitude and direction of biases regarding differences in complexity/severity not discussed. Unclear what differences in usual care existed between rapid and standard WES cohorts.

Strauss (2017)60 NA NA Some risk of bias NA Populations were predominantly old order Amish and Mennonite founder populations; thus some concern that the estimate could be biased.

Tammimies (2015)72 NA NA Low risk of bias NA None


Final



Tan (2017)19 Some risk of bias NA NA Some risk of bias Only conducted sensitivity analysis for the cost of WES testing; assumed that no diagnoses would be made by standard diagnostic pathway, which seems unlikely.


Some risk of bias NA Low risk of bias NA None

Tsiplova (2017)79 NA NA NA Some risk of bias Modeling study, only considered costs of CMA and WES testing, not any other costs associated with the diagnostic trajectory; findings based on assumptions about number of tests conducted and diagnostic yields of 9.3% for CMA and 15.8% for CMA plus WES.

Valencia (2015)54 Some risk of bias NA Low risk of bias NA Retrospectively conducted study; methods for collecting and assessing alterations in management NR.

Vanderver (2016)71 NA NA Low risk of bias NA None

Vissers (2017)20 NA NA Some risk of bias Some risk of bias None

Vrijenhoek (2018)77 NA NA NA High risk of bias None

Waldrop (2019)47 Some risk of bias NA NA NA No information about clinical utility measures were defined and collected.

Walsh (2017)78 NA NA NA High risk of bias Unable to clearly ascertain patient flow through testing strategies for accurate diagnostic yield estimates, comparator strategy based on hypothetical scenario based on several assumptions, no assessment of uncertainty to cover range of assumptions, other limitations present in the cost analysis.

Willing (2015)56 High risk of bias High risk of bias NA NA None

Yang (2014)66 NA NA Low risk of bias NA Enrollment of consecutive patients, > 90% did not opt out of receiving incidental findings and medically actionable variants.

Zhu (2015)45 High risk of bias NA NA NA None



Final


Appendix F. Studies Reporting Diagnostic Yield

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2. Alfares A, Aloraini T, Subaie LA, et al. Whole-genome sequencing offers additional but limited

clinical utility compared with reanalysis of whole-exome sequencing. Genet Med. 2018;20(11):1328-

1333. PMID: 29565419. doi: 10.1038/gim.2018.41

3. Al-Shamsi A, Hertecant JL, Souid AK, Al-Jasmi FA. Whole exome sequencing diagnosis of inborn

errors of metabolism and other disorders in United Arab Emirates. Orphanet J Rare Dis.

2016;11(1):94. PMID: 27391121. doi: 10.1186/s13023-016-0474-3

4. Ammann S, Lehmberg K, Zur Stadt U, et al. Effective Immunological Guidance of Genetic Analyses

Including Exome Sequencing in Patients Evaluated for Hemophagocytic Lymphohistiocytosis. J Clin

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5. Anderson JH, Tester DJ, Will ML, Ackerman MJ. Whole-Exome Molecular Autopsy After Exertion-

Related Sudden Unexplained Death in the Young. Circ Cardiovasc Genet. 2016;9(3):259-265. PMID:

27114410. doi: 10.1161/circgenetics.115.001370

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Final


14. Cordoba M, Rodriguez-Quiroga SA, Vega PA, et al. Whole exome sequencing in neurogenetic

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cause in early onset nephrolithiasis and nephrocalcinosis. Kidney Int. 2018;93(1):204-213. PMID:

28893421. doi: 10.1016/j.kint.2017.06.025

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of Structural and Point Mutations Responsible for Inherited Retinal Dystrophies Revealed by Exome

Sequencing. PLoS One. 2016;11(12):e0168966. PMID: 28005958. doi:

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Final


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Final


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Final


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Final


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Final


Appendix G. Detailed GRADE Assessments

№ of Studies Risk of Bias Inconsistency Indirectness Imprecision Summary of Findings Certainty

Clinical Utility-Actual Changes in Management

1 cohort with modeling; 1 case series; 1 controlled cohort; 28 single-arm observational cohort

Some to High

Serious Not serious Serious Among populations that included diverse phenotypes, medical management changed in 12% to 100% of those who received a molecular diagnosis. Medication changed for 5% to 25% of those who received a diagnosis. Among populations with epilepsy, medical management changed for 0% to 31.3% of patients who received a diagnosis from WES

⨁◯◯◯ VERY LOW

Health Outcomes

1 case series; 1 controlled observational cohort; 5 single-arm observational cohorts

High Serious Not Serious Serious Difference in study designs and ascertainment limit the ability to draw any conclusions about the impact of WES testing on any health outcomes

◯◯◯◯ Unable to determine

Safety Outcomes-ACMG-defined medically actionable variants

21 single-arm observational cohorts; 1 modeling study

Some to High

Not serious Not serious Not serious Across 13 studies with data available for pooling, the pooled result was 3.9% (95% CI, 2.4% to 5.3%). The range across the other studies was reported as 0% to 10%.

⨁⨁◯◯ LOW

Cost outcomes-Cost per Diagnosis

8 cost studies Some to High

Serious Not serious Serious In single-phenotype populations: cost per diagnosis was less in WES pathways compared to the standard diagnostic pathways, and costs were less in early WES pathways compared to WES as a last resort. In diverse phenotype populations: cost per diagnosis was less in early WES pathways compared to WES as a last resort



Final


№ of Studies Risk of Bias Inconsistency Indirectness Imprecision Summary of Findings Certainty

Cost Outcomes-Cost per Additional Diagnosis

7 cost studies Some to High

Serious Not serious Serious For single-phenotype populations: WES was cost-effective when compared to the standard pathway. Early WES cost less and identified more diagnosis in most studies while WES later in the pathway identified additional diagnoses but at an additional cost ($1,775 to $8,550 per additional diagnosis). In diverse phenotypes, early WES cost less and identified more diagnoses when compared to a standard pathway; WES used after some initial evaluation or as a last resort strategy is likely cost-effective (range of estimates suggest cost savings or an additional cost of up to AU$8,112


Cost-effectiveness

1 cost study High Unable to assess, single study body of evidence

Not serious Serious Cost per QALY gained over median 473 days follow-up AU$ -1,578 (95% CI, -205,450 to AU$ 19,780) considering only changes in proband management. Cost per QALY gained AU$ 8,119 (95% CI, AU$ 1,062 to AU$ 38,944) when also considering cascade testing and reproductive counseling in first-degree relatives Modeled over 20 years: Cost per QALY $31,144 for changes in proband management. Cost per QALY gained $14,235 when also considering cascade testing and reproductive outcomes


Abbreviations: AU = Australian; CI = confidence interval; QALY = quality-adjusted life year; WES = whole exome sequencing